import numpy as np # for number crunching
+import pandas as pd # for data handling
+import matplotlib.pyplot as plt # for plotting
+
+from sklearn import datasets # collection of datasets including the iris dataset
+
+#for creating and plotting a picture of the decision tree
+from sklearn import tree 
+import pydotplus
+from IPython.display import Image
+
+# for performance evaluation
+from sklearn.metrics import accuracy_score
+

iris = datasets.load_iris()
+

from sklearn.tree import DecisionTreeClassifier
+

from sklearn.model_selection import train_test_split
+

X = iris.data # it is convention to use a big X for the data 
+              #(as the data is usally a matrix or multi-dimensional array )
+

y = iris.target # it is convention to use a small y for the tartets 
+                #(as the targets are usually a vector one-dimensional array)
+

X
+

array([[5.1, 3.5, 1.4, 0.2],
+       [4.9, 3. , 1.4, 0.2],
+       [4.7, 3.2, 1.3, 0.2],
+       [4.6, 3.1, 1.5, 0.2],
+       [5. , 3.6, 1.4, 0.2],
+       [5.4, 3.9, 1.7, 0.4],
+       [4.6, 3.4, 1.4, 0.3],
+       [5. , 3.4, 1.5, 0.2],
+       [4.4, 2.9, 1.4, 0.2],
+       [4.9, 3.1, 1.5, 0.1],
+       [5.4, 3.7, 1.5, 0.2],
+       [4.8, 3.4, 1.6, 0.2],
+       [4.8, 3. , 1.4, 0.1],
+       [4.3, 3. , 1.1, 0.1],
+       [5.8, 4. , 1.2, 0.2],
+       [5.7, 4.4, 1.5, 0.4],
+       [5.4, 3.9, 1.3, 0.4],
+       [5.1, 3.5, 1.4, 0.3],
+       [5.7, 3.8, 1.7, 0.3],
+       [5.1, 3.8, 1.5, 0.3],
+       [5.4, 3.4, 1.7, 0.2],
+       [5.1, 3.7, 1.5, 0.4],
+       [4.6, 3.6, 1. , 0.2],
+       [5.1, 3.3, 1.7, 0.5],
+       [4.8, 3.4, 1.9, 0.2],
+       [5. , 3. , 1.6, 0.2],
+       [5. , 3.4, 1.6, 0.4],
+       [5.2, 3.5, 1.5, 0.2],
+       [5.2, 3.4, 1.4, 0.2],
+       [4.7, 3.2, 1.6, 0.2],
+       [4.8, 3.1, 1.6, 0.2],
+       [5.4, 3.4, 1.5, 0.4],
+       [5.2, 4.1, 1.5, 0.1],
+       [5.5, 4.2, 1.4, 0.2],
+       [4.9, 3.1, 1.5, 0.2],
+       [5. , 3.2, 1.2, 0.2],
+       [5.5, 3.5, 1.3, 0.2],
+       [4.9, 3.6, 1.4, 0.1],
+       [4.4, 3. , 1.3, 0.2],
+       [5.1, 3.4, 1.5, 0.2],
+       [5. , 3.5, 1.3, 0.3],
+       [4.5, 2.3, 1.3, 0.3],
+       [4.4, 3.2, 1.3, 0.2],
+       [5. , 3.5, 1.6, 0.6],
+       [5.1, 3.8, 1.9, 0.4],
+       [4.8, 3. , 1.4, 0.3],
+       [5.1, 3.8, 1.6, 0.2],
+       [4.6, 3.2, 1.4, 0.2],
+       [5.3, 3.7, 1.5, 0.2],
+       [5. , 3.3, 1.4, 0.2],
+       [7. , 3.2, 4.7, 1.4],
+       [6.4, 3.2, 4.5, 1.5],
+       [6.9, 3.1, 4.9, 1.5],
+       [5.5, 2.3, 4. , 1.3],
+       [6.5, 2.8, 4.6, 1.5],
+       [5.7, 2.8, 4.5, 1.3],
+       [6.3, 3.3, 4.7, 1.6],
+       [4.9, 2.4, 3.3, 1. ],
+       [6.6, 2.9, 4.6, 1.3],
+       [5.2, 2.7, 3.9, 1.4],
+       [5. , 2. , 3.5, 1. ],
+       [5.9, 3. , 4.2, 1.5],
+       [6. , 2.2, 4. , 1. ],
+       [6.1, 2.9, 4.7, 1.4],
+       [5.6, 2.9, 3.6, 1.3],
+       [6.7, 3.1, 4.4, 1.4],
+       [5.6, 3. , 4.5, 1.5],
+       [5.8, 2.7, 4.1, 1. ],
+       [6.2, 2.2, 4.5, 1.5],
+       [5.6, 2.5, 3.9, 1.1],
+       [5.9, 3.2, 4.8, 1.8],
+       [6.1, 2.8, 4. , 1.3],
+       [6.3, 2.5, 4.9, 1.5],
+       [6.1, 2.8, 4.7, 1.2],
+       [6.4, 2.9, 4.3, 1.3],
+       [6.6, 3. , 4.4, 1.4],
+       [6.8, 2.8, 4.8, 1.4],
+       [6.7, 3. , 5. , 1.7],
+       [6. , 2.9, 4.5, 1.5],
+       [5.7, 2.6, 3.5, 1. ],
+       [5.5, 2.4, 3.8, 1.1],
+       [5.5, 2.4, 3.7, 1. ],
+       [5.8, 2.7, 3.9, 1.2],
+       [6. , 2.7, 5.1, 1.6],
+       [5.4, 3. , 4.5, 1.5],
+       [6. , 3.4, 4.5, 1.6],
+       [6.7, 3.1, 4.7, 1.5],
+       [6.3, 2.3, 4.4, 1.3],
+       [5.6, 3. , 4.1, 1.3],
+       [5.5, 2.5, 4. , 1.3],
+       [5.5, 2.6, 4.4, 1.2],
+       [6.1, 3. , 4.6, 1.4],
+       [5.8, 2.6, 4. , 1.2],
+       [5. , 2.3, 3.3, 1. ],
+       [5.6, 2.7, 4.2, 1.3],
+       [5.7, 3. , 4.2, 1.2],
+       [5.7, 2.9, 4.2, 1.3],
+       [6.2, 2.9, 4.3, 1.3],
+       [5.1, 2.5, 3. , 1.1],
+       [5.7, 2.8, 4.1, 1.3],
+       [6.3, 3.3, 6. , 2.5],
+       [5.8, 2.7, 5.1, 1.9],
+       [7.1, 3. , 5.9, 2.1],
+       [6.3, 2.9, 5.6, 1.8],
+       [6.5, 3. , 5.8, 2.2],
+       [7.6, 3. , 6.6, 2.1],
+       [4.9, 2.5, 4.5, 1.7],
+       [7.3, 2.9, 6.3, 1.8],
+       [6.7, 2.5, 5.8, 1.8],
+       [7.2, 3.6, 6.1, 2.5],
+       [6.5, 3.2, 5.1, 2. ],
+       [6.4, 2.7, 5.3, 1.9],
+       [6.8, 3. , 5.5, 2.1],
+       [5.7, 2.5, 5. , 2. ],
+       [5.8, 2.8, 5.1, 2.4],
+       [6.4, 3.2, 5.3, 2.3],
+       [6.5, 3. , 5.5, 1.8],
+       [7.7, 3.8, 6.7, 2.2],
+       [7.7, 2.6, 6.9, 2.3],
+       [6. , 2.2, 5. , 1.5],
+       [6.9, 3.2, 5.7, 2.3],
+       [5.6, 2.8, 4.9, 2. ],
+       [7.7, 2.8, 6.7, 2. ],
+       [6.3, 2.7, 4.9, 1.8],
+       [6.7, 3.3, 5.7, 2.1],
+       [7.2, 3.2, 6. , 1.8],
+       [6.2, 2.8, 4.8, 1.8],
+       [6.1, 3. , 4.9, 1.8],
+       [6.4, 2.8, 5.6, 2.1],
+       [7.2, 3. , 5.8, 1.6],
+       [7.4, 2.8, 6.1, 1.9],
+       [7.9, 3.8, 6.4, 2. ],
+       [6.4, 2.8, 5.6, 2.2],
+       [6.3, 2.8, 5.1, 1.5],
+       [6.1, 2.6, 5.6, 1.4],
+       [7.7, 3. , 6.1, 2.3],
+       [6.3, 3.4, 5.6, 2.4],
+       [6.4, 3.1, 5.5, 1.8],
+       [6. , 3. , 4.8, 1.8],
+       [6.9, 3.1, 5.4, 2.1],
+       [6.7, 3.1, 5.6, 2.4],
+       [6.9, 3.1, 5.1, 2.3],
+       [5.8, 2.7, 5.1, 1.9],
+       [6.8, 3.2, 5.9, 2.3],
+       [6.7, 3.3, 5.7, 2.5],
+       [6.7, 3. , 5.2, 2.3],
+       [6.3, 2.5, 5. , 1.9],
+       [6.5, 3. , 5.2, 2. ],
+       [6.2, 3.4, 5.4, 2.3],
+       [5.9, 3. , 5.1, 1.8]])

y
+

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
+       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
+

X_train
+

array([[6.3, 2.5, 4.9, 1.5],
+       [6.7, 2.5, 5.8, 1.8],
+       [6.2, 3.4, 5.4, 2.3],
+       [5. , 3. , 1.6, 0.2],
+       [4.8, 3.1, 1.6, 0.2],
+       [5.8, 2.8, 5.1, 2.4],
+       [5.5, 4.2, 1.4, 0.2],
+       [6.3, 3.3, 6. , 2.5],
+       [5. , 3.2, 1.2, 0.2],
+       [5.5, 3.5, 1.3, 0.2],
+       [6.2, 2.9, 4.3, 1.3],
+       [6.4, 3.2, 4.5, 1.5],
+       [5.6, 2.9, 3.6, 1.3],
+       [6. , 3.4, 4.5, 1.6],
+       [6.7, 3.1, 4.7, 1.5],
+       [6.7, 3.3, 5.7, 2.1],
+       [6.3, 2.9, 5.6, 1.8],
+       [5.5, 2.6, 4.4, 1.2],
+       [5. , 3.6, 1.4, 0.2],
+       [5.1, 3.8, 1.9, 0.4],
+       [5.9, 3. , 5.1, 1.8],
+       [5.8, 2.6, 4. , 1.2],
+       [5.4, 3.4, 1.7, 0.2],
+       [5.1, 3.8, 1.5, 0.3],
+       [6. , 2.2, 5. , 1.5],
+       [5.5, 2.4, 3.7, 1. ],
+       [4.9, 3.1, 1.5, 0.2],
+       [5.6, 2.5, 3.9, 1.1],
+       [6.6, 2.9, 4.6, 1.3],
+       [6. , 2.2, 4. , 1. ],
+       [6.8, 3. , 5.5, 2.1],
+       [5.1, 3.4, 1.5, 0.2],
+       [6.3, 2.5, 5. , 1.9],
+       [4.8, 3. , 1.4, 0.1],
+       [6.1, 2.6, 5.6, 1.4],
+       [5.1, 3.3, 1.7, 0.5],
+       [5.4, 3. , 4.5, 1.5],
+       [7.2, 3.6, 6.1, 2.5],
+       [6. , 2.9, 4.5, 1.5],
+       [6.3, 3.4, 5.6, 2.4],
+       [5.1, 3.5, 1.4, 0.3],
+       [6.9, 3.1, 5.4, 2.1],
+       [5.5, 2.3, 4. , 1.3],
+       [6.3, 3.3, 4.7, 1.6],
+       [5.7, 4.4, 1.5, 0.4],
+       [6.5, 2.8, 4.6, 1.5],
+       [4.6, 3.1, 1.5, 0.2],
+       [6.4, 2.7, 5.3, 1.9],
+       [7.7, 2.6, 6.9, 2.3],
+       [4.9, 3. , 1.4, 0.2],
+       [5.7, 2.5, 5. , 2. ],
+       [5. , 2. , 3.5, 1. ],
+       [6.5, 3.2, 5.1, 2. ],
+       [5. , 3.5, 1.3, 0.3],
+       [6.3, 2.7, 4.9, 1.8],
+       [6.7, 3.1, 5.6, 2.4],
+       [5. , 3.5, 1.6, 0.6],
+       [5.6, 2.8, 4.9, 2. ],
+       [4.9, 2.5, 4.5, 1.7],
+       [6.5, 3. , 5.2, 2. ],
+       [6.2, 2.8, 4.8, 1.8],
+       [5.5, 2.5, 4. , 1.3],
+       [4.7, 3.2, 1.3, 0.2],
+       [5.9, 3. , 4.2, 1.5],
+       [6.4, 2.8, 5.6, 2.2],
+       [6.7, 3.1, 4.4, 1.4],
+       [4.6, 3.2, 1.4, 0.2],
+       [6.4, 3.2, 5.3, 2.3],
+       [5.6, 2.7, 4.2, 1.3],
+       [5.1, 3.8, 1.6, 0.2],
+       [7.7, 3.8, 6.7, 2.2],
+       [6.8, 2.8, 4.8, 1.4],
+       [5.7, 3.8, 1.7, 0.3],
+       [4.7, 3.2, 1.6, 0.2],
+       [6.9, 3.2, 5.7, 2.3],
+       [5.7, 3. , 4.2, 1.2],
+       [7.3, 2.9, 6.3, 1.8],
+       [6.4, 3.1, 5.5, 1.8],
+       [4.4, 3.2, 1.3, 0.2],
+       [7.2, 3. , 5.8, 1.6],
+       [6.2, 2.2, 4.5, 1.5],
+       [4.5, 2.3, 1.3, 0.3],
+       [6.5, 3. , 5.5, 1.8],
+       [4.9, 3.1, 1.5, 0.1],
+       [5.8, 2.7, 3.9, 1.2],
+       [5.4, 3.9, 1.7, 0.4],
+       [7.1, 3. , 5.9, 2.1],
+       [5.6, 3. , 4.1, 1.3],
+       [5.7, 2.8, 4.5, 1.3],
+       [6.9, 3.1, 5.1, 2.3],
+       [5. , 3.4, 1.6, 0.4],
+       [5.2, 3.5, 1.5, 0.2],
+       [5.6, 3. , 4.5, 1.5],
+       [5.2, 4.1, 1.5, 0.1],
+       [5.1, 2.5, 3. , 1.1],
+       [7.6, 3. , 6.6, 2.1],
+       [6.4, 2.8, 5.6, 2.1],
+       [5.8, 2.7, 5.1, 1.9],
+       [6.9, 3.1, 4.9, 1.5],
+       [4.6, 3.4, 1.4, 0.3],
+       [7.4, 2.8, 6.1, 1.9],
+       [5.8, 2.7, 5.1, 1.9],
+       [5.1, 3.7, 1.5, 0.4],
+       [4.6, 3.6, 1. , 0.2],
+       [4.9, 3.6, 1.4, 0.1],
+       [5.5, 2.4, 3.8, 1.1],
+       [5.9, 3.2, 4.8, 1.8],
+       [5.8, 2.7, 4.1, 1. ],
+       [4.4, 3. , 1.3, 0.2],
+       [6.7, 3. , 5.2, 2.3],
+       [5.1, 3.5, 1.4, 0.2],
+       [6.3, 2.8, 5.1, 1.5],
+       [6.7, 3. , 5. , 1.7],
+       [5.4, 3.4, 1.5, 0.4],
+       [6.4, 2.9, 4.3, 1.3],
+       [6.1, 2.8, 4.7, 1.2],
+       [6.1, 2.9, 4.7, 1.4],
+       [6. , 2.7, 5.1, 1.6],
+       [4.8, 3.4, 1.9, 0.2],
+       [5. , 2.3, 3.3, 1. ]])

y_train
+

array([1, 2, 2, 0, 0, 2, 0, 2, 0, 0, 1, 1, 1, 1, 1, 2, 2, 1, 0, 0, 2, 1,
+       0, 0, 2, 1, 0, 1, 1, 1, 2, 0, 2, 0, 2, 0, 1, 2, 1, 2, 0, 2, 1, 1,
+       0, 1, 0, 2, 2, 0, 2, 1, 2, 0, 2, 2, 0, 2, 2, 2, 2, 1, 0, 1, 2, 1,
+       0, 2, 1, 0, 2, 1, 0, 0, 2, 1, 2, 2, 0, 2, 1, 0, 2, 0, 1, 0, 2, 1,
+       1, 2, 0, 0, 1, 0, 1, 2, 2, 2, 1, 0, 2, 2, 0, 0, 0, 1, 1, 1, 0, 2,
+       0, 2, 1, 0, 1, 1, 1, 1, 0, 1])

X_test
+

array([[5.7, 2.8, 4.1, 1.3],
+       [5.2, 2.7, 3.9, 1.4],
+       [6.7, 3.3, 5.7, 2.5],
+       [7.9, 3.8, 6.4, 2. ],
+       [6.6, 3. , 4.4, 1.4],
+       [7.2, 3.2, 6. , 1.8],
+       [6.8, 3.2, 5.9, 2.3],
+       [5.7, 2.9, 4.2, 1.3],
+       [5.3, 3.7, 1.5, 0.2],
+       [4.8, 3.4, 1.6, 0.2],
+       [6.5, 3. , 5.8, 2.2],
+       [5.8, 4. , 1.2, 0.2],
+       [5.2, 3.4, 1.4, 0.2],
+       [5.7, 2.6, 3.5, 1. ],
+       [5.4, 3.7, 1.5, 0.2],
+       [4.4, 2.9, 1.4, 0.2],
+       [6.1, 3. , 4.9, 1.8],
+       [4.3, 3. , 1.1, 0.1],
+       [5. , 3.3, 1.4, 0.2],
+       [6.1, 3. , 4.6, 1.4],
+       [6.3, 2.3, 4.4, 1.3],
+       [4.9, 2.4, 3.3, 1. ],
+       [5.4, 3.9, 1.3, 0.4],
+       [7.7, 2.8, 6.7, 2. ],
+       [6.1, 2.8, 4. , 1.3],
+       [7. , 3.2, 4.7, 1.4],
+       [5. , 3.4, 1.5, 0.2],
+       [7.7, 3. , 6.1, 2.3],
+       [6. , 3. , 4.8, 1.8],
+       [4.8, 3. , 1.4, 0.3]])

y_test
+

array([1, 1, 2, 2, 1, 2, 2, 1, 0, 0, 2, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 1,
+       0, 2, 1, 1, 0, 2, 2, 0])

df_X_train = pd.DataFrame(X_train, columns = iris.feature_names)  # A dataframe that contains the training data, X
+

colormap = np.array(['Red', 'Blue', 'Green'])
+

visual_X_train = pd.plotting.scatter_matrix(df_X_train, figsize = [10,10], c = colormap[y_train], s = 150)
+

df_X_test = pd.DataFrame(X_test, columns = iris.feature_names) # A dataframe that contains the test data
+

visual_X_test = pd.plotting.scatter_matrix(df_X_test, figsize = [10,10], c = colormap[y_test], s = 150)
+

y_test
+

array([1, 1, 2, 2, 1, 2, 2, 1, 0, 0, 2, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 1,
+       0, 2, 1, 1, 0, 2, 2, 0])

X_2_train, X_2_test, y_2_train, y_2_test = train_test_split(X, y, test_size = 0.2)
+

y_2_test
+

array([1, 2, 1, 1, 1, 2, 0, 1, 0, 1, 2, 2, 0, 1, 2, 0, 2, 2, 2, 0, 1, 2,
+       2, 1, 1, 2, 1, 1, 0, 2])

X_42_train, X_42_test, y_42_train, y_42_test = train_test_split(X, y, test_size = 0.2, random_state = 42)
+

y_42_test
+

array([1, 0, 2, 1, 1, 0, 1, 2, 1, 1, 2, 0, 0, 0, 0, 1, 2, 1, 1, 2, 0, 2,
+       0, 2, 2, 2, 2, 2, 0, 0])

X_23_train, X_23_test, y_23_train, y_23_test = train_test_split(X, y, test_size = 0.2, random_state = 23)
+

y_23_test
+

array([2, 2, 1, 0, 2, 1, 0, 2, 0, 1, 1, 0, 2, 0, 0, 2, 1, 1, 2, 0, 2, 0,
+       0, 0, 2, 0, 0, 2, 1, 1])

model_1 = DecisionTreeClassifier()
+

model_1.fit(X_train,y_train) 
+

DecisionTreeClassifier()

DecisionTreeClassifier()

dot_data_1 = tree.export_graphviz(model_1, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+

graph_1 = pydotplus.graph_from_dot_data(dot_data_1)
+

Image(graph_1.create_png())
+

model_2 = DecisionTreeClassifier()
+model_2.fit(X_2_train,y_2_train) 
+dot_data_2 = tree.export_graphviz(model_2, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+graph_2 = pydotplus.graph_from_dot_data(dot_data_2)
+Image(graph_2.create_png())
+

model_42 = DecisionTreeClassifier()
+model_42.fit(X_42_train,y_42_train) 
+dot_data_42 = tree.export_graphviz(model_42, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+graph_42 = pydotplus.graph_from_dot_data(dot_data_42)
+Image(graph_42.create_png())
+

model_23 = DecisionTreeClassifier()
+model_23.fit(X_23_train,y_23_train) 
+dot_data_23 = tree.export_graphviz(model_23, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+graph_23 = pydotplus.graph_from_dot_data(dot_data_23)
+Image(graph_23.create_png())
+

predictions_train = model_1.predict(X_train) # predicitons on the training set. This should be good, since the model has trained on this data.
+accuracy_score(y_train, predictions_train)
+

1.0

predictions_2_train = model_2.predict(X_2_train) # predicitons on the training set. This should be good, since the model has trained on this data.
+accuracy_score(y_2_train, predictions_2_train)
+

1.0

predictions_42_train = model_42.predict(X_42_train) # predicitons on the training set. This should be good, since the model has trained on this data.
+accuracy_score(y_42_train, predictions_42_train)
+

1.0

predictions_23_train = model_23.predict(X_23_train) # predicitons on the training set. This should be good, since the model has trained on this data.
+accuracy_score(y_23_train, predictions_23_train)
+

1.0

predictions_test = model_1.predict(X_test)
+accuracy_score(y_test, predictions_test)
+

0.9333333333333333

pd.crosstab(y_test, predictions_test, rownames=['labels'], colnames=['prediction'])
+

predictions_2_test = model_2.predict(X_2_test)
+accuracy_score(y_2_test, predictions_2_test)
+

0.9333333333333333

pd.crosstab(y_2_test, predictions_2_test, rownames=['labels'], colnames=['prediction'])
+

predictions_42_test = model_42.predict(X_42_test)
+accuracy_score(y_42_test, predictions_42_test)
+

1.0

pd.crosstab(y_42_test, predictions_42_test, rownames=['labels'], colnames=['prediction'])
+

predictions_23_test = model_23.predict(X_23_test)
+accuracy_score(y_23_test, predictions_23_test)
+

0.9666666666666667

pd.crosstab(y_23_test, predictions_23_test, rownames=['labels'], colnames=['prediction'])
+

model_d2 = DecisionTreeClassifier(max_depth = 2)
+

model_d2.fit(X_42_train,y_42_train) 
+dot_data_d2 = tree.export_graphviz(model_d2, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+graph_d2 = pydotplus.graph_from_dot_data(dot_data_d2)
+Image(graph_d2.create_png())
+

predictions_d2_test = model_d2.predict(X_42_test)
+accuracy_score(y_42_test, predictions_d2_test)
+

0.9666666666666667

pd.crosstab(y_42_test, predictions_d2_test, rownames=['labels'], colnames=['prediction'])
+

model_d3 = DecisionTreeClassifier(max_depth = 3)
+model_d3.fit(X_42_train,y_42_train) 
+dot_data_d3 = tree.export_graphviz(model_d3, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+graph_d3 = pydotplus.graph_from_dot_data(dot_data_d3)
+Image(graph_d3.create_png())
+

predictions_d3_test = model_d3.predict(X_42_test)
+accuracy_score(y_42_test, predictions_d3_test)
+

1.0

from sklearn.model_selection import cross_val_score
+

model_CV = DecisionTreeClassifier()
+

scores_model_CV = cross_val_score(model_CV, X,y, cv=10, scoring = "accuracy")
+

print (scores_model_CV)
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.86666667
+ 0.93333333 1.         1.         1.        ]
+

print (scores_model_CV.mean())
+

0.96
+

model_CV_d2 = DecisionTreeClassifier(max_depth = 2)
+scores_model_CV_d2 = cross_val_score(model_CV_d2, X,y, cv=10, scoring = "accuracy")
+print (scores_model_CV_d2)
+print ('mean:', scores_model_CV_d2.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.86666667
+ 0.86666667 1.         1.         1.        ]
+mean: 0.9533333333333334
+

model_CV_d3 = DecisionTreeClassifier(max_depth = 3)
+scores_model_CV_d3 = cross_val_score(model_CV_d3, X,y, cv=10, scoring = "accuracy")
+print (scores_model_CV_d3)
+print ('mean:', scores_model_CV_d3.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.93333333
+ 0.93333333 0.93333333 1.         1.        ]
+mean: 0.96
+

model_CV_d4 = DecisionTreeClassifier(max_depth = 4)
+scores_model_CV_d4 = cross_val_score(model_CV_d4, X,y, cv=10, scoring = "accuracy")
+print (scores_model_CV_d4)
+print ('mean:', scores_model_CV_d4.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.86666667
+ 0.93333333 0.93333333 1.         1.        ]
+mean: 0.9533333333333334
+

+

model_ss_15 = DecisionTreeClassifier(min_samples_split = 15)
+scores_model_ss_15 = cross_val_score(model_ss_15, X,y, cv=10, scoring = "accuracy")
+print (scores_model_ss_15)
+print ('mean:', scores_model_ss_15.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.93333333
+ 0.93333333 0.93333333 1.         1.        ]
+mean: 0.96
+

+

model_ss_30 = DecisionTreeClassifier(min_samples_split = 30)
+scores_model_ss_30 = cross_val_score(model_ss_30, X,y, cv=10, scoring = "accuracy")
+print (scores_model_ss_30)
+print ('mean:', scores_model_ss_30.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.93333333
+ 0.93333333 0.93333333 1.         1.        ]
+mean: 0.96
+

model_sl_5 = DecisionTreeClassifier(min_samples_leaf = 5)
+scores_model_sl_5 = cross_val_score(model_sl_5, X,y, cv=10, scoring = "accuracy")
+print (scores_model_sl_5)
+print ('mean:', scores_model_sl_5.mean())
+

[1.         0.93333333 1.         1.         0.93333333 0.86666667
+ 0.86666667 1.         1.         1.        ]
+mean: 0.9600000000000002
+

model_sl_4 = DecisionTreeClassifier(min_samples_leaf = 4)
+scores_model_sl_4 = cross_val_score(model_sl_4, X,y, cv=10, scoring = "accuracy")
+print (scores_model_sl_4)
+print ('mean:', scores_model_sl_4.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.93333333
+ 0.86666667 1.         1.         1.        ]
+mean: 0.96
+

model_sl_3 = DecisionTreeClassifier(min_samples_leaf = 3)
+scores_model_sl_3 = cross_val_score(model_sl_3, X,y, cv=10, scoring = "accuracy")
+print (scores_model_sl_3)
+print ('mean:', scores_model_sl_3.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.93333333
+ 0.93333333 1.         1.         1.        ]
+mean: 0.9666666666666666
+

scores_model_CV = cross_val_score(model_CV, X,y, cv=10, scoring = "accuracy")
+print (scores_model_CV)
+print (scores_model_CV.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.86666667
+ 0.93333333 1.         1.         1.        ]
+0.96
+

scores_model_CV = cross_val_score(model_CV, X,y, cv=5, scoring = "accuracy")
+print (scores_model_CV)
+print (scores_model_CV.mean())
+

[0.96666667 0.96666667 0.9        0.96666667 1.        ]
+0.9600000000000002
+

scores_model_CV = cross_val_score(model_CV, X,y, cv=2, scoring = "accuracy")
+print (scores_model_CV)
+print (scores_model_CV.mean())
+

[0.96 0.96]
+0.96
+

scores_model_CV = cross_val_score(model_CV, X,y, cv=50, scoring = "accuracy")
+print (scores_model_CV)
+print (scores_model_CV.mean())
+

[1.         1.         1.         1.         1.         1.
+ 0.66666667 1.         1.         1.         1.         1.
+ 1.         1.         1.         1.         1.         1.
+ 1.         0.66666667 0.66666667 1.         1.         1.
+ 1.         1.         1.         0.66666667 1.         0.66666667
+ 1.         1.         1.         0.66666667 1.         1.
+ 1.         1.         1.         1.         1.         1.
+ 1.         1.         1.         1.         1.         1.
+ 1.         1.        ]
+0.96
+

+

model_sl_3 = DecisionTreeClassifier(min_samples_leaf = 4)
+model_sl_3.fit(X_42_train,y_42_train) 
+dot_data_sl_3 = tree.export_graphviz(model_sl_3, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+graph_sl_3 = pydotplus.graph_from_dot_data(dot_data_sl_3)
+Image(graph_sl_3.create_png())
+

model_ss_44 = DecisionTreeClassifier(min_samples_split = 44)
+model_ss_44.fit(X_42_train,y_42_train) 
+dot_data_ss_44 = tree.export_graphviz(model_ss_44, out_file=None, 
+                     feature_names=['sepal-length','sepal-width','petal-length','petal-width'], 
+                     label='all', rounded=True, filled=True)
+graph_ss_44 = pydotplus.graph_from_dot_data(dot_data_ss_44)
+Image(graph_ss_44.create_png())
+

model_ss_44 = DecisionTreeClassifier(min_samples_split = 44)
+scores_model_ss_44 = cross_val_score(model_ss_44, X,y, cv=10, scoring = "accuracy")
+print (scores_model_ss_44)
+print ('mean:', scores_model_ss_44.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.93333333
+ 0.93333333 1.         1.         1.        ]
+mean: 0.9666666666666666
+

+

+

+

+

from sklearn import preprocessing
+

normalized_X = preprocessing.normalize(X)
+

standardized_X = preprocessing.scale(X)
+

X
+

array([[5.1, 3.5, 1.4, 0.2],
+       [4.9, 3. , 1.4, 0.2],
+       [4.7, 3.2, 1.3, 0.2],
+       [4.6, 3.1, 1.5, 0.2],
+       [5. , 3.6, 1.4, 0.2],
+       [5.4, 3.9, 1.7, 0.4],
+       [4.6, 3.4, 1.4, 0.3],
+       [5. , 3.4, 1.5, 0.2],
+       [4.4, 2.9, 1.4, 0.2],
+       [4.9, 3.1, 1.5, 0.1],
+       [5.4, 3.7, 1.5, 0.2],
+       [4.8, 3.4, 1.6, 0.2],
+       [4.8, 3. , 1.4, 0.1],
+       [4.3, 3. , 1.1, 0.1],
+       [5.8, 4. , 1.2, 0.2],
+       [5.7, 4.4, 1.5, 0.4],
+       [5.4, 3.9, 1.3, 0.4],
+       [5.1, 3.5, 1.4, 0.3],
+       [5.7, 3.8, 1.7, 0.3],
+       [5.1, 3.8, 1.5, 0.3],
+       [5.4, 3.4, 1.7, 0.2],
+       [5.1, 3.7, 1.5, 0.4],
+       [4.6, 3.6, 1. , 0.2],
+       [5.1, 3.3, 1.7, 0.5],
+       [4.8, 3.4, 1.9, 0.2],
+       [5. , 3. , 1.6, 0.2],
+       [5. , 3.4, 1.6, 0.4],
+       [5.2, 3.5, 1.5, 0.2],
+       [5.2, 3.4, 1.4, 0.2],
+       [4.7, 3.2, 1.6, 0.2],
+       [4.8, 3.1, 1.6, 0.2],
+       [5.4, 3.4, 1.5, 0.4],
+       [5.2, 4.1, 1.5, 0.1],
+       [5.5, 4.2, 1.4, 0.2],
+       [4.9, 3.1, 1.5, 0.2],
+       [5. , 3.2, 1.2, 0.2],
+       [5.5, 3.5, 1.3, 0.2],
+       [4.9, 3.6, 1.4, 0.1],
+       [4.4, 3. , 1.3, 0.2],
+       [5.1, 3.4, 1.5, 0.2],
+       [5. , 3.5, 1.3, 0.3],
+       [4.5, 2.3, 1.3, 0.3],
+       [4.4, 3.2, 1.3, 0.2],
+       [5. , 3.5, 1.6, 0.6],
+       [5.1, 3.8, 1.9, 0.4],
+       [4.8, 3. , 1.4, 0.3],
+       [5.1, 3.8, 1.6, 0.2],
+       [4.6, 3.2, 1.4, 0.2],
+       [5.3, 3.7, 1.5, 0.2],
+       [5. , 3.3, 1.4, 0.2],
+       [7. , 3.2, 4.7, 1.4],
+       [6.4, 3.2, 4.5, 1.5],
+       [6.9, 3.1, 4.9, 1.5],
+       [5.5, 2.3, 4. , 1.3],
+       [6.5, 2.8, 4.6, 1.5],
+       [5.7, 2.8, 4.5, 1.3],
+       [6.3, 3.3, 4.7, 1.6],
+       [4.9, 2.4, 3.3, 1. ],
+       [6.6, 2.9, 4.6, 1.3],
+       [5.2, 2.7, 3.9, 1.4],
+       [5. , 2. , 3.5, 1. ],
+       [5.9, 3. , 4.2, 1.5],
+       [6. , 2.2, 4. , 1. ],
+       [6.1, 2.9, 4.7, 1.4],
+       [5.6, 2.9, 3.6, 1.3],
+       [6.7, 3.1, 4.4, 1.4],
+       [5.6, 3. , 4.5, 1.5],
+       [5.8, 2.7, 4.1, 1. ],
+       [6.2, 2.2, 4.5, 1.5],
+       [5.6, 2.5, 3.9, 1.1],
+       [5.9, 3.2, 4.8, 1.8],
+       [6.1, 2.8, 4. , 1.3],
+       [6.3, 2.5, 4.9, 1.5],
+       [6.1, 2.8, 4.7, 1.2],
+       [6.4, 2.9, 4.3, 1.3],
+       [6.6, 3. , 4.4, 1.4],
+       [6.8, 2.8, 4.8, 1.4],
+       [6.7, 3. , 5. , 1.7],
+       [6. , 2.9, 4.5, 1.5],
+       [5.7, 2.6, 3.5, 1. ],
+       [5.5, 2.4, 3.8, 1.1],
+       [5.5, 2.4, 3.7, 1. ],
+       [5.8, 2.7, 3.9, 1.2],
+       [6. , 2.7, 5.1, 1.6],
+       [5.4, 3. , 4.5, 1.5],
+       [6. , 3.4, 4.5, 1.6],
+       [6.7, 3.1, 4.7, 1.5],
+       [6.3, 2.3, 4.4, 1.3],
+       [5.6, 3. , 4.1, 1.3],
+       [5.5, 2.5, 4. , 1.3],
+       [5.5, 2.6, 4.4, 1.2],
+       [6.1, 3. , 4.6, 1.4],
+       [5.8, 2.6, 4. , 1.2],
+       [5. , 2.3, 3.3, 1. ],
+       [5.6, 2.7, 4.2, 1.3],
+       [5.7, 3. , 4.2, 1.2],
+       [5.7, 2.9, 4.2, 1.3],
+       [6.2, 2.9, 4.3, 1.3],
+       [5.1, 2.5, 3. , 1.1],
+       [5.7, 2.8, 4.1, 1.3],
+       [6.3, 3.3, 6. , 2.5],
+       [5.8, 2.7, 5.1, 1.9],
+       [7.1, 3. , 5.9, 2.1],
+       [6.3, 2.9, 5.6, 1.8],
+       [6.5, 3. , 5.8, 2.2],
+       [7.6, 3. , 6.6, 2.1],
+       [4.9, 2.5, 4.5, 1.7],
+       [7.3, 2.9, 6.3, 1.8],
+       [6.7, 2.5, 5.8, 1.8],
+       [7.2, 3.6, 6.1, 2.5],
+       [6.5, 3.2, 5.1, 2. ],
+       [6.4, 2.7, 5.3, 1.9],
+       [6.8, 3. , 5.5, 2.1],
+       [5.7, 2.5, 5. , 2. ],
+       [5.8, 2.8, 5.1, 2.4],
+       [6.4, 3.2, 5.3, 2.3],
+       [6.5, 3. , 5.5, 1.8],
+       [7.7, 3.8, 6.7, 2.2],
+       [7.7, 2.6, 6.9, 2.3],
+       [6. , 2.2, 5. , 1.5],
+       [6.9, 3.2, 5.7, 2.3],
+       [5.6, 2.8, 4.9, 2. ],
+       [7.7, 2.8, 6.7, 2. ],
+       [6.3, 2.7, 4.9, 1.8],
+       [6.7, 3.3, 5.7, 2.1],
+       [7.2, 3.2, 6. , 1.8],
+       [6.2, 2.8, 4.8, 1.8],
+       [6.1, 3. , 4.9, 1.8],
+       [6.4, 2.8, 5.6, 2.1],
+       [7.2, 3. , 5.8, 1.6],
+       [7.4, 2.8, 6.1, 1.9],
+       [7.9, 3.8, 6.4, 2. ],
+       [6.4, 2.8, 5.6, 2.2],
+       [6.3, 2.8, 5.1, 1.5],
+       [6.1, 2.6, 5.6, 1.4],
+       [7.7, 3. , 6.1, 2.3],
+       [6.3, 3.4, 5.6, 2.4],
+       [6.4, 3.1, 5.5, 1.8],
+       [6. , 3. , 4.8, 1.8],
+       [6.9, 3.1, 5.4, 2.1],
+       [6.7, 3.1, 5.6, 2.4],
+       [6.9, 3.1, 5.1, 2.3],
+       [5.8, 2.7, 5.1, 1.9],
+       [6.8, 3.2, 5.9, 2.3],
+       [6.7, 3.3, 5.7, 2.5],
+       [6.7, 3. , 5.2, 2.3],
+       [6.3, 2.5, 5. , 1.9],
+       [6.5, 3. , 5.2, 2. ],
+       [6.2, 3.4, 5.4, 2.3],
+       [5.9, 3. , 5.1, 1.8]])

normalized_X
+

array([[0.80377277, 0.55160877, 0.22064351, 0.0315205 ],
+       [0.82813287, 0.50702013, 0.23660939, 0.03380134],
+       [0.80533308, 0.54831188, 0.2227517 , 0.03426949],
+       [0.80003025, 0.53915082, 0.26087943, 0.03478392],
+       [0.790965  , 0.5694948 , 0.2214702 , 0.0316386 ],
+       [0.78417499, 0.5663486 , 0.2468699 , 0.05808704],
+       [0.78010936, 0.57660257, 0.23742459, 0.0508767 ],
+       [0.80218492, 0.54548574, 0.24065548, 0.0320874 ],
+       [0.80642366, 0.5315065 , 0.25658935, 0.03665562],
+       [0.81803119, 0.51752994, 0.25041771, 0.01669451],
+       [0.80373519, 0.55070744, 0.22325977, 0.02976797],
+       [0.786991  , 0.55745196, 0.26233033, 0.03279129],
+       [0.82307218, 0.51442011, 0.24006272, 0.01714734],
+       [0.8025126 , 0.55989251, 0.20529392, 0.01866308],
+       [0.81120865, 0.55945424, 0.16783627, 0.02797271],
+       [0.77381111, 0.59732787, 0.2036345 , 0.05430253],
+       [0.79428944, 0.57365349, 0.19121783, 0.05883625],
+       [0.80327412, 0.55126656, 0.22050662, 0.04725142],
+       [0.8068282 , 0.53788547, 0.24063297, 0.04246464],
+       [0.77964883, 0.58091482, 0.22930848, 0.0458617 ],
+       [0.8173379 , 0.51462016, 0.25731008, 0.03027177],
+       [0.78591858, 0.57017622, 0.23115252, 0.06164067],
+       [0.77577075, 0.60712493, 0.16864581, 0.03372916],
+       [0.80597792, 0.52151512, 0.26865931, 0.07901744],
+       [0.776114  , 0.54974742, 0.30721179, 0.03233808],
+       [0.82647451, 0.4958847 , 0.26447184, 0.03305898],
+       [0.79778206, 0.5424918 , 0.25529026, 0.06382256],
+       [0.80641965, 0.54278246, 0.23262105, 0.03101614],
+       [0.81609427, 0.5336001 , 0.21971769, 0.03138824],
+       [0.79524064, 0.54144043, 0.27072022, 0.03384003],
+       [0.80846584, 0.52213419, 0.26948861, 0.03368608],
+       [0.82225028, 0.51771314, 0.22840286, 0.06090743],
+       [0.76578311, 0.60379053, 0.22089897, 0.0147266 ],
+       [0.77867447, 0.59462414, 0.19820805, 0.02831544],
+       [0.81768942, 0.51731371, 0.25031309, 0.03337508],
+       [0.82512295, 0.52807869, 0.19802951, 0.03300492],
+       [0.82699754, 0.52627116, 0.19547215, 0.03007264],
+       [0.78523221, 0.5769053 , 0.22435206, 0.01602515],
+       [0.80212413, 0.54690282, 0.23699122, 0.03646019],
+       [0.80779568, 0.53853046, 0.23758697, 0.03167826],
+       [0.80033301, 0.56023311, 0.20808658, 0.04801998],
+       [0.86093857, 0.44003527, 0.24871559, 0.0573959 ],
+       [0.78609038, 0.57170209, 0.23225397, 0.03573138],
+       [0.78889479, 0.55222635, 0.25244633, 0.09466737],
+       [0.76693897, 0.57144472, 0.28572236, 0.06015208],
+       [0.82210585, 0.51381615, 0.23978087, 0.05138162],
+       [0.77729093, 0.57915795, 0.24385598, 0.030482  ],
+       [0.79594782, 0.55370283, 0.24224499, 0.03460643],
+       [0.79837025, 0.55735281, 0.22595384, 0.03012718],
+       [0.81228363, 0.5361072 , 0.22743942, 0.03249135],
+       [0.76701103, 0.35063361, 0.51499312, 0.15340221],
+       [0.74549757, 0.37274878, 0.52417798, 0.17472599],
+       [0.75519285, 0.33928954, 0.53629637, 0.16417236],
+       [0.75384916, 0.31524601, 0.54825394, 0.17818253],
+       [0.7581754 , 0.32659863, 0.5365549 , 0.17496355],
+       [0.72232962, 0.35482858, 0.57026022, 0.16474184],
+       [0.72634846, 0.38046824, 0.54187901, 0.18446945],
+       [0.75916547, 0.37183615, 0.51127471, 0.15493173],
+       [0.76301853, 0.33526572, 0.53180079, 0.15029153],
+       [0.72460233, 0.37623583, 0.54345175, 0.19508524],
+       [0.76923077, 0.30769231, 0.53846154, 0.15384615],
+       [0.73923462, 0.37588201, 0.52623481, 0.187941  ],
+       [0.78892752, 0.28927343, 0.52595168, 0.13148792],
+       [0.73081412, 0.34743622, 0.56308629, 0.16772783],
+       [0.75911707, 0.3931142 , 0.48800383, 0.17622361],
+       [0.76945444, 0.35601624, 0.50531337, 0.16078153],
+       [0.70631892, 0.37838513, 0.5675777 , 0.18919257],
+       [0.75676497, 0.35228714, 0.53495455, 0.13047672],
+       [0.76444238, 0.27125375, 0.55483721, 0.18494574],
+       [0.76185188, 0.34011245, 0.53057542, 0.14964948],
+       [0.6985796 , 0.37889063, 0.56833595, 0.21312598],
+       [0.77011854, 0.35349703, 0.50499576, 0.16412362],
+       [0.74143307, 0.29421947, 0.57667016, 0.17653168],
+       [0.73659895, 0.33811099, 0.56754345, 0.14490471],
+       [0.76741698, 0.34773582, 0.51560829, 0.15588157],
+       [0.76785726, 0.34902603, 0.51190484, 0.16287881],
+       [0.76467269, 0.31486523, 0.53976896, 0.15743261],
+       [0.74088576, 0.33173989, 0.55289982, 0.18798594],
+       [0.73350949, 0.35452959, 0.55013212, 0.18337737],
+       [0.78667474, 0.35883409, 0.48304589, 0.13801311],
+       [0.76521855, 0.33391355, 0.52869645, 0.15304371],
+       [0.77242925, 0.33706004, 0.51963422, 0.14044168],
+       [0.76434981, 0.35581802, 0.51395936, 0.15814134],
+       [0.70779525, 0.31850786, 0.60162596, 0.1887454 ],
+       [0.69333409, 0.38518561, 0.57777841, 0.1925928 ],
+       [0.71524936, 0.40530797, 0.53643702, 0.19073316],
+       [0.75457341, 0.34913098, 0.52932761, 0.16893434],
+       [0.77530021, 0.28304611, 0.54147951, 0.15998258],
+       [0.72992443, 0.39103094, 0.53440896, 0.16944674],
+       [0.74714194, 0.33960997, 0.54337595, 0.17659719],
+       [0.72337118, 0.34195729, 0.57869695, 0.15782644],
+       [0.73260391, 0.36029701, 0.55245541, 0.1681386 ],
+       [0.76262994, 0.34186859, 0.52595168, 0.1577855 ],
+       [0.76986879, 0.35413965, 0.5081134 , 0.15397376],
+       [0.73544284, 0.35458851, 0.55158213, 0.1707278 ],
+       [0.73239618, 0.38547167, 0.53966034, 0.15418867],
+       [0.73446047, 0.37367287, 0.5411814 , 0.16750853],
+       [0.75728103, 0.3542121 , 0.52521104, 0.15878473],
+       [0.78258054, 0.38361791, 0.4603415 , 0.16879188],
+       [0.7431482 , 0.36505526, 0.5345452 , 0.16948994],
+       [0.65387747, 0.34250725, 0.62274045, 0.25947519],
+       [0.69052512, 0.32145135, 0.60718588, 0.22620651],
+       [0.71491405, 0.30207636, 0.59408351, 0.21145345],
+       [0.69276796, 0.31889319, 0.61579374, 0.1979337 ],
+       [0.68619022, 0.31670318, 0.61229281, 0.232249  ],
+       [0.70953708, 0.28008043, 0.61617694, 0.1960563 ],
+       [0.67054118, 0.34211284, 0.61580312, 0.23263673],
+       [0.71366557, 0.28351098, 0.61590317, 0.17597233],
+       [0.71414125, 0.26647062, 0.61821183, 0.19185884],
+       [0.69198788, 0.34599394, 0.58626751, 0.24027357],
+       [0.71562645, 0.3523084 , 0.56149152, 0.22019275],
+       [0.71576546, 0.30196356, 0.59274328, 0.21249287],
+       [0.71718148, 0.31640359, 0.58007326, 0.22148252],
+       [0.6925518 , 0.30375079, 0.60750157, 0.24300063],
+       [0.67767924, 0.32715549, 0.59589036, 0.28041899],
+       [0.69589887, 0.34794944, 0.57629125, 0.25008866],
+       [0.70610474, 0.3258945 , 0.59747324, 0.1955367 ],
+       [0.69299099, 0.34199555, 0.60299216, 0.19799743],
+       [0.70600618, 0.2383917 , 0.63265489, 0.21088496],
+       [0.72712585, 0.26661281, 0.60593821, 0.18178146],
+       [0.70558934, 0.32722984, 0.58287815, 0.23519645],
+       [0.68307923, 0.34153961, 0.59769433, 0.24395687],
+       [0.71486543, 0.25995106, 0.62202576, 0.18567933],
+       [0.73122464, 0.31338199, 0.56873028, 0.20892133],
+       [0.69595601, 0.3427843 , 0.59208198, 0.21813547],
+       [0.71529453, 0.31790868, 0.59607878, 0.17882363],
+       [0.72785195, 0.32870733, 0.56349829, 0.21131186],
+       [0.71171214, 0.35002236, 0.57170319, 0.21001342],
+       [0.69594002, 0.30447376, 0.60894751, 0.22835532],
+       [0.73089855, 0.30454106, 0.58877939, 0.1624219 ],
+       [0.72766159, 0.27533141, 0.59982915, 0.18683203],
+       [0.71578999, 0.34430405, 0.5798805 , 0.18121266],
+       [0.69417747, 0.30370264, 0.60740528, 0.2386235 ],
+       [0.72366005, 0.32162669, 0.58582004, 0.17230001],
+       [0.69385414, 0.29574111, 0.63698085, 0.15924521],
+       [0.73154399, 0.28501714, 0.57953485, 0.21851314],
+       [0.67017484, 0.36168166, 0.59571097, 0.2553047 ],
+       [0.69804799, 0.338117  , 0.59988499, 0.196326  ],
+       [0.71066905, 0.35533453, 0.56853524, 0.21320072],
+       [0.72415258, 0.32534391, 0.56672811, 0.22039426],
+       [0.69997037, 0.32386689, 0.58504986, 0.25073566],
+       [0.73337886, 0.32948905, 0.54206264, 0.24445962],
+       [0.69052512, 0.32145135, 0.60718588, 0.22620651],
+       [0.69193502, 0.32561648, 0.60035539, 0.23403685],
+       [0.68914871, 0.33943145, 0.58629069, 0.25714504],
+       [0.72155725, 0.32308533, 0.56001458, 0.24769876],
+       [0.72965359, 0.28954508, 0.57909015, 0.22005426],
+       [0.71653899, 0.3307103 , 0.57323119, 0.22047353],
+       [0.67467072, 0.36998072, 0.58761643, 0.25028107],
+       [0.69025916, 0.35097923, 0.5966647 , 0.21058754]])

standardized_X
+

array([[-9.00681170e-01,  1.01900435e+00, -1.34022653e+00,
+        -1.31544430e+00],
+       [-1.14301691e+00, -1.31979479e-01, -1.34022653e+00,
+        -1.31544430e+00],
+       [-1.38535265e+00,  3.28414053e-01, -1.39706395e+00,
+        -1.31544430e+00],
+       [-1.50652052e+00,  9.82172869e-02, -1.28338910e+00,
+        -1.31544430e+00],
+       [-1.02184904e+00,  1.24920112e+00, -1.34022653e+00,
+        -1.31544430e+00],
+       [-5.37177559e-01,  1.93979142e+00, -1.16971425e+00,
+        -1.05217993e+00],
+       [-1.50652052e+00,  7.88807586e-01, -1.34022653e+00,
+        -1.18381211e+00],
+       [-1.02184904e+00,  7.88807586e-01, -1.28338910e+00,
+        -1.31544430e+00],
+       [-1.74885626e+00, -3.62176246e-01, -1.34022653e+00,
+        -1.31544430e+00],
+       [-1.14301691e+00,  9.82172869e-02, -1.28338910e+00,
+        -1.44707648e+00],
+       [-5.37177559e-01,  1.47939788e+00, -1.28338910e+00,
+        -1.31544430e+00],
+       [-1.26418478e+00,  7.88807586e-01, -1.22655167e+00,
+        -1.31544430e+00],
+       [-1.26418478e+00, -1.31979479e-01, -1.34022653e+00,
+        -1.44707648e+00],
+       [-1.87002413e+00, -1.31979479e-01, -1.51073881e+00,
+        -1.44707648e+00],
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+        -1.31544430e+00],
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+        -1.05217993e+00],
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+       [-1.02184904e+00,  7.88807586e-01, -1.22655167e+00,
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+        -1.31544430e+00],
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+        -1.31544430e+00],
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+        -1.31544430e+00],
+       [-5.37177559e-01,  7.88807586e-01, -1.28338910e+00,
+        -1.05217993e+00],
+       [-7.79513300e-01,  2.40018495e+00, -1.28338910e+00,
+        -1.44707648e+00],
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+        -1.31544430e+00],
+       [-1.14301691e+00,  9.82172869e-02, -1.28338910e+00,
+        -1.31544430e+00],
+       [-1.02184904e+00,  3.28414053e-01, -1.45390138e+00,
+        -1.31544430e+00],
+       [-4.16009689e-01,  1.01900435e+00, -1.39706395e+00,
+        -1.31544430e+00],
+       [-1.14301691e+00,  1.24920112e+00, -1.34022653e+00,
+        -1.44707648e+00],
+       [-1.74885626e+00, -1.31979479e-01, -1.39706395e+00,
+        -1.31544430e+00],
+       [-9.00681170e-01,  7.88807586e-01, -1.28338910e+00,
+        -1.31544430e+00],
+       [-1.02184904e+00,  1.01900435e+00, -1.39706395e+00,
+        -1.18381211e+00],
+       [-1.62768839e+00, -1.74335684e+00, -1.39706395e+00,
+        -1.18381211e+00],
+       [-1.74885626e+00,  3.28414053e-01, -1.39706395e+00,
+        -1.31544430e+00],
+       [-1.02184904e+00,  1.01900435e+00, -1.22655167e+00,
+        -7.88915558e-01],
+       [-9.00681170e-01,  1.70959465e+00, -1.05603939e+00,
+        -1.05217993e+00],
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+        -1.18381211e+00],
+       [-9.00681170e-01,  1.70959465e+00, -1.22655167e+00,
+        -1.31544430e+00],
+       [-1.50652052e+00,  3.28414053e-01, -1.34022653e+00,
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+       [-6.58345429e-01,  1.47939788e+00, -1.28338910e+00,
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+       [-1.02184904e+00,  5.58610819e-01, -1.34022653e+00,
+        -1.31544430e+00],
+       [ 1.40150837e+00,  3.28414053e-01,  5.35408562e-01,
+         2.64141916e-01],
+       [ 6.74501145e-01,  3.28414053e-01,  4.21733708e-01,
+         3.95774101e-01],
+       [ 1.28034050e+00,  9.82172869e-02,  6.49083415e-01,
+         3.95774101e-01],
+       [-4.16009689e-01, -1.74335684e+00,  1.37546573e-01,
+         1.32509732e-01],
+       [ 7.95669016e-01, -5.92373012e-01,  4.78571135e-01,
+         3.95774101e-01],
+       [-1.73673948e-01, -5.92373012e-01,  4.21733708e-01,
+         1.32509732e-01],
+       [ 5.53333275e-01,  5.58610819e-01,  5.35408562e-01,
+         5.27406285e-01],
+       [-1.14301691e+00, -1.51316008e+00, -2.60315415e-01,
+        -2.62386821e-01],
+       [ 9.16836886e-01, -3.62176246e-01,  4.78571135e-01,
+         1.32509732e-01],
+       [-7.79513300e-01, -8.22569778e-01,  8.07091462e-02,
+         2.64141916e-01],
+       [-1.02184904e+00, -2.43394714e+00, -1.46640561e-01,
+        -2.62386821e-01],
+       [ 6.86617933e-02, -1.31979479e-01,  2.51221427e-01,
+         3.95774101e-01],
+       [ 1.89829664e-01, -1.97355361e+00,  1.37546573e-01,
+        -2.62386821e-01],
+       [ 3.10997534e-01, -3.62176246e-01,  5.35408562e-01,
+         2.64141916e-01],
+       [-2.94841818e-01, -3.62176246e-01, -8.98031345e-02,
+         1.32509732e-01],
+       [ 1.03800476e+00,  9.82172869e-02,  3.64896281e-01,
+         2.64141916e-01],
+       [-2.94841818e-01, -1.31979479e-01,  4.21733708e-01,
+         3.95774101e-01],
+       [-5.25060772e-02, -8.22569778e-01,  1.94384000e-01,
+        -2.62386821e-01],
+       [ 4.32165405e-01, -1.97355361e+00,  4.21733708e-01,
+         3.95774101e-01],
+       [-2.94841818e-01, -1.28296331e+00,  8.07091462e-02,
+        -1.30754636e-01],
+       [ 6.86617933e-02,  3.28414053e-01,  5.92245988e-01,
+         7.90670654e-01],
+       [ 3.10997534e-01, -5.92373012e-01,  1.37546573e-01,
+         1.32509732e-01],
+       [ 5.53333275e-01, -1.28296331e+00,  6.49083415e-01,
+         3.95774101e-01],
+       [ 3.10997534e-01, -5.92373012e-01,  5.35408562e-01,
+         8.77547895e-04],
+       [ 6.74501145e-01, -3.62176246e-01,  3.08058854e-01,
+         1.32509732e-01],
+       [ 9.16836886e-01, -1.31979479e-01,  3.64896281e-01,
+         2.64141916e-01],
+       [ 1.15917263e+00, -5.92373012e-01,  5.92245988e-01,
+         2.64141916e-01],
+       [ 1.03800476e+00, -1.31979479e-01,  7.05920842e-01,
+         6.59038469e-01],
+       [ 1.89829664e-01, -3.62176246e-01,  4.21733708e-01,
+         3.95774101e-01],
+       [-1.73673948e-01, -1.05276654e+00, -1.46640561e-01,
+        -2.62386821e-01],
+       [-4.16009689e-01, -1.51316008e+00,  2.38717193e-02,
+        -1.30754636e-01],
+       [-4.16009689e-01, -1.51316008e+00, -3.29657076e-02,
+        -2.62386821e-01],
+       [-5.25060772e-02, -8.22569778e-01,  8.07091462e-02,
+         8.77547895e-04],
+       [ 1.89829664e-01, -8.22569778e-01,  7.62758269e-01,
+         5.27406285e-01],
+       [-5.37177559e-01, -1.31979479e-01,  4.21733708e-01,
+         3.95774101e-01],
+       [ 1.89829664e-01,  7.88807586e-01,  4.21733708e-01,
+         5.27406285e-01],
+       [ 1.03800476e+00,  9.82172869e-02,  5.35408562e-01,
+         3.95774101e-01],
+       [ 5.53333275e-01, -1.74335684e+00,  3.64896281e-01,
+         1.32509732e-01],
+       [-2.94841818e-01, -1.31979479e-01,  1.94384000e-01,
+         1.32509732e-01],
+       [-4.16009689e-01, -1.28296331e+00,  1.37546573e-01,
+         1.32509732e-01],
+       [-4.16009689e-01, -1.05276654e+00,  3.64896281e-01,
+         8.77547895e-04],
+       [ 3.10997534e-01, -1.31979479e-01,  4.78571135e-01,
+         2.64141916e-01],
+       [-5.25060772e-02, -1.05276654e+00,  1.37546573e-01,
+         8.77547895e-04],
+       [-1.02184904e+00, -1.74335684e+00, -2.60315415e-01,
+        -2.62386821e-01],
+       [-2.94841818e-01, -8.22569778e-01,  2.51221427e-01,
+         1.32509732e-01],
+       [-1.73673948e-01, -1.31979479e-01,  2.51221427e-01,
+         8.77547895e-04],
+       [-1.73673948e-01, -3.62176246e-01,  2.51221427e-01,
+         1.32509732e-01],
+       [ 4.32165405e-01, -3.62176246e-01,  3.08058854e-01,
+         1.32509732e-01],
+       [-9.00681170e-01, -1.28296331e+00, -4.30827696e-01,
+        -1.30754636e-01],
+       [-1.73673948e-01, -5.92373012e-01,  1.94384000e-01,
+         1.32509732e-01],
+       [ 5.53333275e-01,  5.58610819e-01,  1.27429511e+00,
+         1.71209594e+00],
+       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,
+         9.22302838e-01],
+       [ 1.52267624e+00, -1.31979479e-01,  1.21745768e+00,
+         1.18556721e+00],
+       [ 5.53333275e-01, -3.62176246e-01,  1.04694540e+00,
+         7.90670654e-01],
+       [ 7.95669016e-01, -1.31979479e-01,  1.16062026e+00,
+         1.31719939e+00],
+       [ 2.12851559e+00, -1.31979479e-01,  1.61531967e+00,
+         1.18556721e+00],
+       [-1.14301691e+00, -1.28296331e+00,  4.21733708e-01,
+         6.59038469e-01],
+       [ 1.76501198e+00, -3.62176246e-01,  1.44480739e+00,
+         7.90670654e-01],
+       [ 1.03800476e+00, -1.28296331e+00,  1.16062026e+00,
+         7.90670654e-01],
+       [ 1.64384411e+00,  1.24920112e+00,  1.33113254e+00,
+         1.71209594e+00],
+       [ 7.95669016e-01,  3.28414053e-01,  7.62758269e-01,
+         1.05393502e+00],
+       [ 6.74501145e-01, -8.22569778e-01,  8.76433123e-01,
+         9.22302838e-01],
+       [ 1.15917263e+00, -1.31979479e-01,  9.90107977e-01,
+         1.18556721e+00],
+       [-1.73673948e-01, -1.28296331e+00,  7.05920842e-01,
+         1.05393502e+00],
+       [-5.25060772e-02, -5.92373012e-01,  7.62758269e-01,
+         1.58046376e+00],
+       [ 6.74501145e-01,  3.28414053e-01,  8.76433123e-01,
+         1.44883158e+00],
+       [ 7.95669016e-01, -1.31979479e-01,  9.90107977e-01,
+         7.90670654e-01],
+       [ 2.24968346e+00,  1.70959465e+00,  1.67215710e+00,
+         1.31719939e+00],
+       [ 2.24968346e+00, -1.05276654e+00,  1.78583195e+00,
+         1.44883158e+00],
+       [ 1.89829664e-01, -1.97355361e+00,  7.05920842e-01,
+         3.95774101e-01],
+       [ 1.28034050e+00,  3.28414053e-01,  1.10378283e+00,
+         1.44883158e+00],
+       [-2.94841818e-01, -5.92373012e-01,  6.49083415e-01,
+         1.05393502e+00],
+       [ 2.24968346e+00, -5.92373012e-01,  1.67215710e+00,
+         1.05393502e+00],
+       [ 5.53333275e-01, -8.22569778e-01,  6.49083415e-01,
+         7.90670654e-01],
+       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,
+         1.18556721e+00],
+       [ 1.64384411e+00,  3.28414053e-01,  1.27429511e+00,
+         7.90670654e-01],
+       [ 4.32165405e-01, -5.92373012e-01,  5.92245988e-01,
+         7.90670654e-01],
+       [ 3.10997534e-01, -1.31979479e-01,  6.49083415e-01,
+         7.90670654e-01],
+       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,
+         1.18556721e+00],
+       [ 1.64384411e+00, -1.31979479e-01,  1.16062026e+00,
+         5.27406285e-01],
+       [ 1.88617985e+00, -5.92373012e-01,  1.33113254e+00,
+         9.22302838e-01],
+       [ 2.49201920e+00,  1.70959465e+00,  1.50164482e+00,
+         1.05393502e+00],
+       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,
+         1.31719939e+00],
+       [ 5.53333275e-01, -5.92373012e-01,  7.62758269e-01,
+         3.95774101e-01],
+       [ 3.10997534e-01, -1.05276654e+00,  1.04694540e+00,
+         2.64141916e-01],
+       [ 2.24968346e+00, -1.31979479e-01,  1.33113254e+00,
+         1.44883158e+00],
+       [ 5.53333275e-01,  7.88807586e-01,  1.04694540e+00,
+         1.58046376e+00],
+       [ 6.74501145e-01,  9.82172869e-02,  9.90107977e-01,
+         7.90670654e-01],
+       [ 1.89829664e-01, -1.31979479e-01,  5.92245988e-01,
+         7.90670654e-01],
+       [ 1.28034050e+00,  9.82172869e-02,  9.33270550e-01,
+         1.18556721e+00],
+       [ 1.03800476e+00,  9.82172869e-02,  1.04694540e+00,
+         1.58046376e+00],
+       [ 1.28034050e+00,  9.82172869e-02,  7.62758269e-01,
+         1.44883158e+00],
+       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,
+         9.22302838e-01],
+       [ 1.15917263e+00,  3.28414053e-01,  1.21745768e+00,
+         1.44883158e+00],
+       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,
+         1.71209594e+00],
+       [ 1.03800476e+00, -1.31979479e-01,  8.19595696e-01,
+         1.44883158e+00],
+       [ 5.53333275e-01, -1.28296331e+00,  7.05920842e-01,
+         9.22302838e-01],
+       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,
+         1.05393502e+00],
+       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,
+         1.44883158e+00],
+       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,
+         7.90670654e-01]])

df_X = pd.DataFrame(X, columns = ['sepal_length',
+ 'sepal_width',
+  'petal_length',
+ 'petal_width'])
+df_X.head()
+

visual_X = pd.plotting.scatter_matrix(df_X, figsize = [10,10], c = colormap[y], s = 150)
+

df_X_normalized = pd.DataFrame(normalized_X, columns = ['sepal_length',
+ 'sepal_width',
+  'petal_length',
+ 'petal_width'])
+df_X_normalized.head()
+

df_X_standardized = pd.DataFrame(standardized_X, columns = ['sepal_length',
+ 'sepal_width',
+  'petal_length',
+ 'petal_width'])
+df_X_standardized.head()
+

visual_X_standardized = pd.plotting.scatter_matrix(df_X_standardized, figsize = [10,10], c = colormap[y], s = 150)
+

model_normalized_CV = DecisionTreeClassifier()
+scores_model_normalized_CV = cross_val_score(model_normalized_CV, normalized_X,y, cv=10, scoring = "accuracy")
+print (scores_model_normalized_CV)
+print (scores_model_normalized_CV.mean())
+

[1.         0.93333333 0.93333333 1.         0.93333333 1.
+ 0.86666667 1.         0.86666667 1.        ]
+0.9533333333333334
+

model_standardized_CV = DecisionTreeClassifier()
+scores_model_standardized_CV = cross_val_score(model_standardized_CV, standardized_X,y, cv=10, scoring = "accuracy")
+print (scores_model_standardized_CV)
+print (scores_model_standardized_CV.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.86666667
+ 0.93333333 1.         1.         1.        ]
+0.96
+

model_CV = DecisionTreeClassifier()
+scores_model_CV = cross_val_score(model_CV,X,y, cv=10, scoring = "accuracy")
+print (scores_model_CV)
+print (scores_model_CV.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.86666667
+ 0.93333333 1.         1.         1.        ]
+0.96
+

model_standardized_CV_ss_44 = DecisionTreeClassifier(min_samples_split = 44)
+scores_model_standardized_CV_ss_44 = cross_val_score(model_standardized_CV_ss_44, standardized_X,y, cv=10, scoring = "accuracy")
+print (scores_model_standardized_CV_ss_44)
+print (scores_model_standardized_CV_ss_44.mean())
+

[1.         0.93333333 1.         0.93333333 0.93333333 0.93333333
+ 0.93333333 1.         1.         1.        ]
+0.9666666666666666
+

+

+

import seaborn as sns
+

pairplot.set(xlim=(0,10), ylim = (0,10))
+

<seaborn.axisgrid.PairGrid at 0x1e4cada04d0>

iris_sns = sns.load_dataset("iris")
+

sns.pairplot(iris_sns, hue = 'species') 
+# you get a warning about something that will be removed from the function in the future. We can ignore that for now.
+# after the warning the pairplot will appear.
+

C:\Users\stei\AppData\Local\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
+  with pd.option_context('mode.use_inf_as_na', True):
+C:\Users\stei\AppData\Local\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
+  with pd.option_context('mode.use_inf_as_na', True):
+C:\Users\stei\AppData\Local\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
+  with pd.option_context('mode.use_inf_as_na', True):
+C:\Users\stei\AppData\Local\anaconda3\Lib\site-packages\seaborn\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
+  with pd.option_context('mode.use_inf_as_na', True):
+

<seaborn.axisgrid.PairGrid at 0x1e4d5ff64d0>

+

	Internal LIMS ID	Original ID	group	HPRA000767	HPRA034083	HPRA006876	HPRA019035	HPRA003490	HPRA022019	HPRA017192
0	GLA_02-0001	HD1	0	0.352329	-0.074663	0.442451	0.210802	-0.025678	-0.923763	0.028253
1	GLA_02-0002	GC1	1	2.184865	0.467075	1.272995	1.857599	2.709290	1.367955	0.220041
2	GLA_02-0003	HD2	0	0.519969	-0.358698	0.124177	1.383003	1.227493	-0.128889	-0.343792
3	GLA_02-0004	GC2	1	1.155189	0.467075	0.630671	0.080847	-0.025678	-0.416995	0.753164
4	GLA_02-0005	HD3	0	-0.590507	-0.074663	-1.423605	-1.591214	-0.375025	-0.165849	-1.292029

Decision Tree Classifier for the IRIS dataset¶

Let's build a Decision Tree Classifier¶

Performance Analysis of our trees¶

A short note on cross validation:¶

Feature scaling¶

You can also use the seaborn library to plot.¶

	sepal_length	sepal_width	petal_length	petal_width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

	sepal_length	sepal_width	petal_length	petal_width
0	0.803773	0.551609	0.220644	0.031521
1	0.828133	0.507020	0.236609	0.033801
2	0.805333	0.548312	0.222752	0.034269
3	0.800030	0.539151	0.260879	0.034784
4	0.790965	0.569495	0.221470	0.031639

	sepal_length	sepal_width	petal_length	petal_width
0	-0.900681	1.019004	-1.340227	-1.315444
1	-1.143017	-0.131979	-1.340227	-1.315444
2	-1.385353	0.328414	-1.397064	-1.315444
3	-1.506521	0.098217	-1.283389	-1.315444
4	-1.021849	1.249201	-1.340227	-1.315444

prediction	0	1	2
labels
0	11	0	0
1	0	9	1
2	0	1	8

prediction	0	1	2
labels
0	9	0	0
1	0	10	0
2	0	2	9

prediction	0	1	2
labels
0	10	0	0
1	0	9	0
2	0	0	11

prediction	0	1	2
labels
0	12	0	0
1	0	8	0
2	0	1	9

	sepal_length	sepal_width	petal_length	petal_width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2

	sepal_length	sepal_width	petal_length	petal_width
0	5.1	3.5	1.4	0.2
1	4.9	3.0	1.4	0.2
2	4.7	3.2	1.3	0.2
3	4.6	3.1	1.5	0.2
4	5.0	3.6	1.4	0.2