Imports

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

Data

from sklearn.datasets import load_iris
iris = load_iris(as_frame=True)
df = iris.data
df["target"] = iris.target

df.head()

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	target
0	5.1	3.5	1.4	0.2	0
1	4.9	3.0	1.4	0.2	0
2	4.7	3.2	1.3	0.2	0
3	4.6	3.1	1.5	0.2	0
4	5.0	3.6	1.4	0.2	0

df.tail(10)

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	target
140	6.7	3.1	5.6	2.4	2
141	6.9	3.1	5.1	2.3	2
142	5.8	2.7	5.1	1.9	2
143	6.8	3.2	5.9	2.3	2
144	6.7	3.3	5.7	2.5	2
145	6.7	3.0	5.2	2.3	2
146	6.3	2.5	5.0	1.9	2
147	6.5	3.0	5.2	2.0	2
148	6.2	3.4	5.4	2.3	2
149	5.9	3.0	5.1	1.8	2

Train-Test Split

from sklearn.model_selection import train_test_split

X = df.drop('target', axis=1)
y = df['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10)

Decision Tree

from sklearn.tree import DecisionTreeClassifier

dtree = DecisionTreeClassifier(max_depth=2)

dtree.fit(X_train,y_train)

DecisionTreeClassifier(max_depth=2)

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Voorspelling en score

y_pred = dtree.predict(X_test)

from sklearn.metrics import classification_report, confusion_matrix

print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00         5
           1       1.00      0.80      0.89         5
           2       0.83      1.00      0.91         5

    accuracy                           0.93        15
   macro avg       0.94      0.93      0.93        15
weighted avg       0.94      0.93      0.93        15

print(confusion_matrix(y_test, y_pred))

[[5 0 0]
 [0 4 1]
 [0 0 5]]

Visualisatie

from IPython.display import Image  
from six import StringIO  
from sklearn.tree import export_graphviz
import pydot 

features = list(df.columns[0:4])
features

['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']

dot_data = StringIO()  
export_graphviz(dtree, out_file=dot_data, feature_names=features, filled=True, rounded=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())  
Image(graph[0].create_png())

Random Forest

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier(n_estimators=500, n_jobs=-1)
rfc.fit(X_train, y_train)

RandomForestClassifier(n_estimators=500, n_jobs=-1)

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rfc_pred = rfc.predict(X_test)

print(confusion_matrix(y_test,rfc_pred))

[[19  0  0]
 [ 0 14  0]
 [ 0  2 10]]

print(classification_report(y_test,rfc_pred))

              precision    recall  f1-score   support

           0       1.00      1.00      1.00        19
           1       0.88      1.00      0.93        14
           2       1.00      0.83      0.91        12

    accuracy                           0.96        45
   macro avg       0.96      0.94      0.95        45
weighted avg       0.96      0.96      0.96        45

for score, name in zip(rfc.feature_importances_, features):
    print(round(score, 2), name)

0.1 sepal length (cm)
0.03 sepal width (cm)
0.43 petal length (cm)
0.44 petal width (cm)