Random Forests and Ensemble Learning

Ensemble learning is a method that combines multiple models to improve the overall performance. Random Forests are an example of ensemble learning because they combine multiple decision trees to produce a more accurate and robust prediction.

Bootstrapping involves sampling with replacement to create multiple datasets, while bagging involves creating multiple models with the same data and predictors. Random Forests use these techniques to solve the issue of similar models by introducing randomness in the selection of predictors and data, resulting in a more diverse set of models.

A residual is the difference between the observed and predicted values of a dependent variable. A positive residual indicates that the model underpredicts the value, while a negative residual indicates that the model overpredicts the value.

<p>Accuracy is not suitable for regression models because it only measures the proportion of correct predictions, ignoring the magnitude of the errors. Instead, we use metrics like SSE, MSE, and RMSE to evaluate the performance of regression models, which measure the magnitude of the errors.</p> Signup and view all the answers

<p>The Regression Tree algorithm stops when the tree reaches a certain depth or when the nodes are pure (i.e., all observations have the same value). The leaf nodes in the Regression Tree algorithm represent the predicted values of the dependent variable, and they are calculated based on the values of the input variables.</p> Signup and view all the answers

<p>Random Forest Regressor model works in a similar way to RandomForestClassifier, but it uses Regression Decision Trees instead of Classification Decision Trees, and it also uses bagging.</p> Signup and view all the answers

<p>The main objective of SVMs is to find the widest possible street (margin) between classes, and this is done to maximize the separation between classes and reduce the risk of overfitting.</p> Signup and view all the answers

<p>The cost C hyperparameter controls the regularization strength of the SVM model. To prevent overfitting, you would increase C, which increases the regularization strength and reduces the risk of overfitting.</p> Signup and view all the answers

<p>The kernel trick is a method to transform the input data into a higher-dimensional space, where it is easier to find a linear hyperplane. This helps in creating an SVM model by allowing it to handle non-linearly separable data.</p> Signup and view all the answers

<p>Logistic regression is called regression because it uses a regression-like approach to predict probabilities. The range of predictions from the S-curve is between 0 and 1.</p> Signup and view all the answers