A decision tree can be used for regression as well as classification, more information about it can be found here. The below code will help to create the decision tree model for regression.
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import pandas as pd import numpy as np ColumnNames=['Hours','Calories', 'Weight'] DataValues=[[ 1.0, 2500, 95], [ 2.0, 2000, 85], [ 2.5, 1900, 83], [ 3.0, 1850, 81], [ 3.5, 1600, 80], [ 4.0, 1500, 78], [ 5.0, 1500, 77], [ 5.5, 1600, 80], [ 6.0, 1700, 75], [ 6.5, 1500, 70]] #Create the Data Frame GymData=pd.DataFrame(data=DataValues,columns=ColumnNames) GymData.head() #Separate Target Variable and Predictor Variables TargetVariable='Weight' Predictors=['Hours','Calories'] X=GymData[Predictors].values y=GymData[TargetVariable].values #Split the data into training and testing set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ######################################################## ###### Single Decision Tree Regression in Python ####### from sklearn import tree #choose from different tunable hyper parameters RegModel = tree.DecisionTreeRegressor(max_depth=3,criterion='mse') #Printing all the parameters of Decision Tree print(RegModel) #Creating the model on Training Data DTree=RegModel.fit(X_train,y_train) prediction=DTree.predict(X_test) #Measuring Goodness of fit in Training data from sklearn import metrics print('R2 Value:',metrics.r2_score(y_train, DTree.predict(X_train))) #Measuring accuracy on Testing Data print('Accuracy',100- (np.mean(np.abs((y_test - prediction) / y_test)) * 100)) #Plotting the feature importance for Top 10 most important columns %matplotlib inline feature_importances = pd.Series(DTree.feature_importances_, index=Predictors) feature_importances.nlargest(10).plot(kind='barh') #Printing some sample values of prediction TestingDataResults=pd.DataFrame(data=X_test, columns=Predictors) TestingDataResults[TargetVariable]=y_test TestingDataResults[('Predicted'+TargetVariable)]=prediction TestingDataResults.head() |
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