54 lines
1.8 KiB
Plaintext
54 lines
1.8 KiB
Plaintext
# Load libraries
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import pandas as pd
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from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier
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from sklearn.model_selection import train_test_split # Import train_test_split function
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from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation
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from sklearn.tree import export_graphviz
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from sklearn.externals.six import StringIO
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from IPython.display import Image
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import pydotplus
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col_names = ['age', 'sex', 'fat', 'spicy', 'hungry', 'budget']
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# load dataset
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pima = pd.read_csv("nazwa.csv", header=None, names=col_names)
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#split dataset in features and target variable
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feature_cols = ['age', 'sex', 'fat', 'spicy']
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X = pima[feature_cols] # Features
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y = pima.label # Target variable
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# Split dataset into training set and test set
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test
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# Create Decision Tree classifer object
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clf = DecisionTreeClassifier()
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# Train Decision Tree Classifer
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clf = clf.fit(X_train,y_train)
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#Predict the response for test dataset
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y_pred = clf.predict(X_test)
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# Model Accuracy, how often is the classifier correct?
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print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
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dot_data = StringIO()
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export_graphviz(clf, out_file=dot_data,
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filled=True, rounded=True,
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special_characters=True,feature_names = feature_cols,class_names=['0','1'])
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graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
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graph.write_png('food_tree.png')
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Image(graph.create_png())
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# Create Decision Tree classifer object
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clf = DecisionTreeClassifier(criterion="entropy", max_depth=3)
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# Train Decision Tree Classifer
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clf = clf.fit(X_train,y_train)
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#Predict the response for test dataset
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y_pred = clf.predict(X_test)
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# Model Accuracy, how often is the classifier correct?
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print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) |