How is feature importance calculated in bagging?Review LaterFeature importance is obtained by aggregating the feature importance scores from each individual model.Feature importance is calculated by considering the number of times a feature is selected for splitting across all the weak learners.Feature importance is calculated based on the weights assigned to each feature during training.Feature importance is randomly assigned in bagging and not calculated explicitly.

What is the purpose of bagging in machine learning?Review LaterTo reduce bias in the model's predictions.To increase the complexity of weak learners.To improve the stability and accuracy of the model.To reduce the computational complexity of the training process.

Consider the code snippet that extracts and prints the feature importances from a trained random forest regressor model. The model is used to predict Standard_yield based on various features. The code utilises the feature_importances_ attribute of the random forest model to obtain importance scores for each feature.Which of the following statements best describes the purpose and outcome of the provided code snippet?OptionsThe code identifies and prints the importance scores for each feature in the random forest model, indicating how much each feature contributes to the model's ability to predict Standard_yield. Higher scores suggest a greater contribution to the prediction.The code counts the number of times each feature is used to split the data across all trees in the random forest, thereby determining each feature's importance in predicting Standard_yield.The code calculates and prints the coefficient values for each feature used in the random forest, model to predict Standard_yield, indicating the strength and direction of the relationship between each feature and the target variable.The code computes the correlation between each feature and the target variable Standard_yield, printing a list of features sorted by their correlation coefficients to identify the most relevant predictors.

Following the training of our random forest models, we decide to analyse the feature importance scores provided by the model built using 200 trees. Our aim is to identify which features the model considers most significant in predicting the target variable.Which of the following does the model consider to be the top 3 most significant features in predicting Standard_yield?Rainfall, Crop_type_tea, LatitudeElevation, Soil_fertility, pHpH, Rainfall, Location_Rural_Hawassa Soil_fertility, Rainfall, Slope

Which of the following machine learning algorithm is based upon the idea of bagging?Random-forestRegressionClassificationDecision treeSAVE

Which of the following machine learning algorithm is based upon the idea of bagging?(1 Point)Decision treeRandom-forestClassificationRegression