【sklearn】tree.DecisionTreeClassifier

from sklearn import tree
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split


wine = load_wine()
wine.data

''' array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00, 1.065e+03], [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00, 1.050e+03], [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00, 1.185e+03], ..., [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00, 8.350e+02], [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00, 8.400e+02], [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00, 5.600e+02]]) '''

# pd see 
import pandas as pd
pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)], axis=1)

	0	1	2	3	4	5	6	7	8	9	10	11	12	0
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0	0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0	0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0	0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0	0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
173	13.71	5.65	2.45	20.5	95.0	1.68	0.61	0.52	1.06	7.70	0.64	1.74	740.0	2
174	13.40	3.91	2.48	23.0	102.0	1.80	0.75	0.43	1.41	7.30	0.70	1.56	750.0	2
175	13.27	4.28	2.26	20.0	120.0	1.59	0.69	0.43	1.35	10.20	0.59	1.56	835.0	2
176	13.17	2.59	2.37	20.0	120.0	1.65	0.68	0.53	1.46	9.30	0.60	1.62	840.0	2
177	14.13	4.10	2.74	24.5	96.0	2.05	0.76	0.56	1.35	9.20	0.61	1.60	560.0	2

178 rows × 14 columns

#  Divide the training test set 
Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data, wine.target, test_size=0.3)


'''  Decision tree model  random_state splitter=‘best’  Select the most important features to branch , if random Then random branches , Reduce overfitting   Pruning prevents over fitting  max_depth Limit the maximum depth  min_samples_leaf=0.05 If there are less than nodes after branching 0.05* Total , It doesn't Branch  min_samples_split=5 If the node contains less than 5 Samples are unbranched   Reduce dimension and prevent over fitting  max_feature Consider the number of features in place  min_impurity_decrease Limit information gain  '''
clf = tree.DecisionTreeClassifier(criterion='gini'
                                  ,random_state=3
                                  ,max_depth=3
                                  ,min_samples_leaf=0.05
                                  ,min_samples_split=2
                                 )
clf = clf.fit(Xtrain, Ytrain)
score = clf.score(Xtest, Ytest)
score

''' 0.8888888888888888 '''

# clf.feature_importances_ View feature weights  *zip() After aggregation, return 
[*zip(wine.feature_names, clf.feature_importances_)]
''' [('alcohol', 0.44232962250700664), ('malic_acid', 0.0), ('ash', 0.0), ('alcalinity_of_ash', 0.0), ('magnesium', 0.0), ('total_phenols', 0.0), ('flavanoids', 0.4126765806025175), ('nonflavanoid_phenols', 0.0), ('proanthocyanins', 0.0), ('color_intensity', 0.0), ('hue', 0.0030484226577791127), ('od280/od315_of_diluted_wines', 0.1419453742326969), ('proline', 0.0)] '''

#  Hyperparametric learning curve 
import matplotlib.pyplot as plt
test = []
for i in range(10):
    clf = tree.DecisionTreeClassifier(max_depth=i+1
                                      ,criterion='entropy'
                                      ,random_state=30
                                      ,splitter='random'
                                     )
    clf = clf.fit(Xtrain, Ytrain)
    score = clf.score(Xtest, Ytest)
    test.append(score)
plt.plot(range(1, 11), test, color='red', label='max_depth')
plt.legend() #  Add legend 
plt.show