A Random Forest is a bagging algorithm created by combining multiple decision trees together. More information about this algorithm can be found here.
You can learn more about Random Forests in the below video.
The below code will help to create a random forest model for regression use cases.
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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) ###### Random Forest Regression in Python ####### from sklearn.ensemble import RandomForestRegressor RegModel = RandomForestRegressor(n_estimators=100,criterion='mse') #Printing all the parameters of Random Forest print(RegModel) #Creating the model on Training Data RF=RegModel.fit(X_train,y_train) prediction=RF.predict(X_test) #Measuring Goodness of fit in Training data from sklearn import metrics print('R2 Value:',metrics.r2_score(y_train, RF.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(RF.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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