The XGboost is a boosting algorithm used in supervised machine learning, more information about it can be found here.
You can learn more about XGBoost algorithm in the below video.
The below code will help to create XGboost regression model.
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#### Create Gym Data for regression in Python #### 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) ############################################################## ###### Xgboost Regression in Python ####### from xgboost import XGBRegressor RegModel=XGBRegressor(max_depth=3, learning_rate=0.1, n_estimators=500, objective='reg:linear', booster='gbtree') #Printing all the parameters of XGBoost print(RegModel) #Creating the model on Training Data XGB=RegModel.fit(X_train,y_train) prediction=XGB.predict(X_test) #Measuring Goodness of fit in Training data from sklearn import metrics print('R2 Value:',metrics.r2_score(y_train, XGB.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(XGB.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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