Здравствуйте, люди, чтобы списать деревья решений с помощью Python в качестве вывода Следующий код может быть реализован: –
Раньше, выполняя код Python загрузить набор данных из следующей ссылки: https://github.com/ruthvikraja/dt.git.git.
# Decision Tree Classifier import pandas as pd from sklearn.model_selection import train_test_split # This is used to split our data into training and testing sets from sklearn import tree # Here tree is a module from sklearn.metrics import accuracy_score # Used to check the goodness of our model import matplotlib.pyplot as plt # Used to plot figures df1=pd.read_excel("/Users/ruthvikrajam.v/Desktop/heart.xlsx") # storing our excel file in df1 df1.info() # This function is used to check whether our data consists of any missing or null values X=df1.loc[:,df1.columns!="target"] y=df1["target"] X_train, X_test, Y_train, Y_test=train_test_split(X, y, test_size=0.2, random_state=0) # Here test_size = 0.2 means it uses 20% of our input data for testing and 80% for training set # random_state = 0 means every time it uses the same set of testing and training set for evaluation clftree1=tree.DecisionTreeClassifier(criterion="entropy") # Using Entropy for computing the Decision Tree clftree1.fit(X_train,Y_train) pred=clftree1.predict(X_test) # Predicting the values for our test data accuracy_score1=accuracy_score(Y_test, pred) # Finding the accuracy score of our model print(accuracy_score1) fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (10,10),dpi=300) # Let us create a figure with size (10X10) and density per inch = 300 tree.plot_tree(clftree1, feature_names=list(df1.columns),class_names="01",filled =True) # plot_tree is used to plot our decision tree. The parameters are our Decision Tree, feature names, class names to be displayed in # string format (or) as a list, filled=True will automatically fill colours to our tree etc fig.savefig("imagename1.jpeg.png") clftree2=tree.DecisionTreeClassifier(criterion="gini") # Using Gini Index for computing the Decision Tree clftree2.fit(X_train,Y_train) pred=clftree2.predict(X_test) # Predicting the values for our test data accuracy_score2=accuracy_score(Y_test, pred) # Finding the accuracy score of our model print(accuracy_score2) fig, ax = plt.subplots(nrows = 1,ncols = 1,figsize = (10,10), dpi=300) tree.plot_tree(clftree2, feature_names=list(df1.columns), class_names="01", filled=True) fig.savefig('imagename2.jpeg.png')
Сделанный…
Оригинал: “https://dev.to/ruthvikraja_mv/plotting-decision-trees-using-python-3g3d”