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

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.

features = predictorsimportances = model.feature_importances_indices = np.argsort(importances)feat_importances = pd.Series(model.feature_importances_, index=predictors.columns)feat_importances.nlargest(30).plot(kind='barh')#Final Features from Random Forest (Select Features with highest feature importance)rf_top_features = pd.DataFrame(feat_importances.nlargest(47)).axes[0].tolist()rf_top_features

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 a characteristic of Random Forests?Each tree is trained on a random subset of the featuresAll trees are identicalThey are sensitive to feature scalingThey are prone to overfitting

In this challenge, we want to test how our data fits to a random forest model and other functionalities that come with its such as analysing feature importance.We are required to write a function named train_rf_model that trains and tests a random forest model on a given dataset. Our function should do the following:Take a RandomForestRegressor object (with any desired hyperparameters set) as input,Separate the features X and target y dataframesSplit the data into training and testing sets - use a test size of 20% and a random state of 42 for reproducibilityFit the model to the training dataMake predictions on the testing setReturn the trained model, the R-squared score, and the Mean Squared Error (MSE) of the test set predictions.Question 26Implement the function outlined above.Using the function, train a random forest model on our dataset with random_state set to 42, and max_depth=15, while leaving all other hyperparameters at their defaults. Use all the features available in the encoded dataset for this task. What are the R-squared and MSE scores for the model on the test data?OptionsR2: 0.0059, MSE: 0.6198R2: 0.5555, MSE: 0.2345R2: 0.9586, MSE: 0.0006R2: 0.8196, MSE: 0.0500