Gradient Boosting Overview Quiz

<p>Recommendation systems (C)</p> Signup and view all the answers

<p>It can only handle numerical features. (A)</p> Signup and view all the answers

<p>Decision tree (A)</p> Signup and view all the answers

<p>To improve predictions iteratively when combined. (C)</p> Signup and view all the answers

<p>Tabular data (C)</p> Signup and view all the answers

<p>$156 (A)</p> Signup and view all the answers

<p>The differences between predicted values and observed values (B)</p> Signup and view all the answers

<p>It is limited to four leaves in this case (B)</p> Signup and view all the answers

<p>Mean Squared Error (MSE) (A)</p> Signup and view all the answers

<p>It determines how much each weak learner contributes to the ensemble (C)</p> Signup and view all the answers

<p>It requires building more trees for effective training (B)</p> Signup and view all the answers

<p>It enhances the overall performance of the ensemble (D)</p> Signup and view all the answers

<p>8 to 32 nodes (A)</p> Signup and view all the answers

<p>Increases the chances of overfitting (B)</p> Signup and view all the answers

<p>10 (D)</p> Signup and view all the answers

<p>Higher values enable the trees to create splits based on more data points (D)</p> Signup and view all the answers

<p>Can lead to faster training and potential overfitting (B)</p> Signup and view all the answers

<p>0.5 to 1 (B)</p> Signup and view all the answers

<p>The tree has three split levels (C)</p> Signup and view all the answers

<p>Reduces the risk of overfitting (C)</p> Signup and view all the answers

<p>Makes the model more complex and computationally expensive (D)</p> Signup and view all the answers

<p>To learn from training data and generalize to unseen data (A)</p> Signup and view all the answers

<p>Mean Squared Error (MSE) (D)</p> Signup and view all the answers

<p>It quantifies the difference between predicted outputs and actual values (A)</p> Signup and view all the answers

<p>The average of the target values (B)</p> Signup and view all the answers

<p>It allows comparison of loss across datasets to assess generalization ability (A)</p> Signup and view all the answers

<p>Cross-entropy (D)</p> Signup and view all the answers

<p>Incremental learning from errors (D)</p> Signup and view all the answers

<p>It helps establish the model’s predictive power (D)</p> Signup and view all the answers

A powerful ensemble technique in machine learning that combines predictions from multiple weak learners to create a more accurate strong learner.

A machine learning model that performs better than random guessing, but still has room for improvement.

The most common weak learner used in gradient boosting, known for its ability to handle any data type.

The primary input for the Gradient Boosting Algorithm, consisting of features and a target variable.

A technique that builds a model sequentially by adding weak learners to improve accuracy.

Predicting whether a customer will stop using a service.

Developing models to personalize recommendations for users based on their preferences.

Using machine learning to assess the risk of a financial loan being defaulted.

A function that measures the difference between a model's predictions and actual values, indicating how well the model is performing.

A common loss function used in regression tasks, where it calculates the sum of squared differences between predicted and actual values.

A loss function used in classification tasks. It measures the difference between two probability distributions, comparing the predicted probabilities to the actual class probabilities.

The starting point for gradient boosting, where the initial prediction is simply the average of the target values in the training data.

The ability of a machine learning model to perform well on unseen data, ensuring it generalizes well beyond the training data.

A process in which a model learns from the training data to make predictions on unseen data. The goal is to make the model generalize well to new data points.

A set of features used to predict the target variable. In this case, customer age, purchase category, and purchase weight are used to predict purchase amount.

The average of all purchase values in a dataset, serving as the initial prediction in Gradient Boosting.

Differences between the observed purchase values and the initial prediction. They indicate how far off our initial prediction is from the real data.

The process of training multiple weak learners sequentially, each one trying to correct the errors of the previous ones. This leads to a more powerful, combined model called a "strong learner."

A crucial parameter controlling the contribution of each weak learner to the final model. It regulates the learning speed and prevents overfitting.

The total number of weak learners used to build the final strong learner. More trees usually result in a more powerful and accurate model.

The process of finding the optimal values for the hyperparameters, like the learning rate and number of trees, to maximize the model's performance.

Controls the depth of each tree in a gradient boosting model. A higher depth allows the model to capture more complex patterns, but can also lead to overfitting. Choosing a depth between 3 and 10 is recommended.

Determines the minimum number of samples required to create a split in a decision tree. Larger minimums prevent overfitting by ensuring the branches split on a sufficient amount of data.

Controls what portion of the data is used to train each tree. Lower subsampling rates can speed up training but increase the chance of overfitting.

Similar to subsampling, but applies to features instead of rows. It helps to avoid overfitting when working with high-dimensional data by simplifying the features used for each tree.