Ensemble Learning and Decision Trees Quiz

A machine learning technique that combines multiple 'weak' learner models to improve the overall prediction accuracy.

Multiple base models are trained independently on different subsets of the training data.

The process of combining the predictions of multiple models into a single prediction.

When aggregating regression models, averaging the individual predictions is used to get a more accurate estimate.

When aggregating classification models, the class with the majority of votes is chosen as the final prediction.

Improves prediction accuracy by reducing the variance of the model.

Bagging can be easily implemented using libraries like scikit-learn, enabling quick integration into various machine learning projects.

Bagging has been applied in various fields like healthcare, IT, environment, and finance to improve prediction accuracy and address complex problems.

A fully grown decision tree may overfit the training data, leading to poor performance on new data. Pruning involves removing decision nodes from the bottom of the tree to improve accuracy.

It involves splitting the training data into two sets: training data (D) and validation data (V). The tree is built using D and then trimmed based on its performance on V to optimize accuracy.

Ensemble learning is a technique that combines multiple base classifiers (models) to create a more robust and accurate classifier. It aims to reduce bias and variance in predictions.

Bagging (bootstrap aggregation) is an ensemble method that reduces variance by creating multiple training sets with random sampling with replacement. It trains separate models on these sets, and their predictions are combined to improve accuracy.

Bagging aims to create a diverse set of models by generating multiple training sets with replacement. Each set may contain repeated instances of some data points.

The random forest algorithm is an extension of bagging that uses both bagging and feature randomness to create a diverse set of decision trees.

Bootstrapping is a resampling technique used in bagging. It involves creating multiple training sets by randomly selecting data points with replacement from the original dataset.

Each bootstrap sample may contain duplicate data points because the sampling occurs with replacement meaning a data point can be selected multiple times.

A statistical measure of income inequality in a population. Values closer to 0 indicate greater equality, while values closer to 1 indicate greater inequality.

A modification of Information Gain for decision tree construction, used to overcome the bias towards attributes with more values. Gain Ratio considers both information gain and the number of branches in a split.

One of the oldest tree classification methods, it uses chi-squared statistics to evaluate the significance of differences between parent and child nodes. It finds out if the differences between sub-nodes and parent node are statistically significant.

A technique used to prevent decision trees from overfitting to the training data. Pruning involves simplifying the tree by removing unnecessary branches or nodes.

A method of building multiple decision trees and combining their predictions to improve overall accuracy. It aims to reduce the variance of the model.

A technique used to construct multiple decision trees where each tree considers a random subset of features and samples. It helps to reduce the variance of the model.

A concept used to reduce overfitting in decision trees. It involves stopping the splitting process before the tree reaches full growth.

A process in random forest where each decision tree is trained on a randomly sampled subset of the original dataset. This helps reduce the correlation between trees and improves the overall model's stability.

A technique used in random forests where a random subset of features is selected for each decision tree. This helps to create uncorrelated trees and reduces the risk of overfitting.

A setting in random forest algorithms that controls the size of each decision tree node. A larger node size indicates a simpler tree with less detail, while a smaller node size allows for more complex trees but may increase the risk of overfitting.

A parameter in random forest algorithms that controls the number of decision trees used in the ensemble. Increasing the number of trees generally improves accuracy but also increases training time and memory consumption.

A hyperparameter in random forest algorithms that determines the number of features randomly sampled for each decision tree. A smaller number of features can lead to simpler trees, while a larger number of features can lead to more complex trees.

Random forests can be sensitive to changes in data, such as noise, environmental shifts, and parameter adjustments. This can negatively impact model performance.

While increasing the number of trees in a random forest can boost accuracy, it also requires more time and resources to train the model. This can be a challenge when dealing with large datasets or limited computational power.

A boosting technique that utilizes the gradient descent method to optimize each predictor sequentially, with the goal of creating the strongest model possible.

A powerful variant of Gradient Boosting designed for efficiency and scalability on large datasets.

A variant of Gradient Boosting that introduces randomness by subsampling the data before each iteration, enhancing performance and preventing overfitting.

A variation of Gradient Boosting that uses histogram-based techniques for splitting data, leading to faster and more memory-efficient performance.

Boosting algorithms like Gradient Boosting are easy to implement and use. They often provide built-in mechanisms for handling missing data, making them convenient for machine learning tasks.

Boosting techniques can be customized by tuning their hyperparameters. This allows for a greater level of control and optimization, leading to improved model performance.

Boosting methods are adept at handling data with missing values. They are designed to handle real-world data effectively, where missing values are common.

Boosting techniques are widely used in various domains, such as healthcare, finance, and environmental science. They offer robust and accurate predictions, aiding in tackling complex problems.

A machine learning technique that combines multiple base models to improve prediction accuracy. It trains each base model on different subsets of the training data, then combines their predictions.

A technique that combines multiple base models and trains a meta-model to learn how to best combine their predictions.

A type of ensemble learning where the models learn from each other's mistakes sequentially. Each model focuses on the examples where the previous models were wrong, becoming progressively more specialized.

Creating multiple training datasets by randomly sampling data points with replacement. It involves selecting data points from the original dataset, allowing for duplicates in each sample.

A method where a single training dataset is split into "k" folds. Each of the "k" models is trained on (k-1) folds and then used to predict the remaining fold. The results from the "k" models are then combined to create the final model.

A type of ensemble model where individual models learn from each other in a sequential manner. Each model learns from the errors of the previous models, focusing on the examples where the prior models performed poorly.

A set of models that use different algorithms or configurations to solve the same problem. They are designed to leverage diverse perspectives and improve overall prediction accuracy.