Precision measures the ratio of true positive predictions to the total predicted positives, indicating the accuracy of positive predictions.

True Positive refers to the cases where the model correctly predicts the positive class.

Precision measures the ratio of true positives to the total predicted positives, indicating the accuracy of positive predictions.

ROC stands for Receiver Operating Characteristic, which is a graphical representation of a classifier's performance.

The curse of dimensionality encompasses all these challenges, making clustering in high-dimensional spaces difficult.

Density-based clustering forms clusters based on the number of points in a specified region, allowing for arbitrary shapes.

Stride refers to the step size of the filter during convolution, determining how much the filter moves across the input image.

Feature importance refers to the contribution of a feature to the model's predictions, indicating how much it helps in making accurate decisions.

Pruning is the process of removing branches from a Decision Tree to prevent overfitting and improve generalization.

Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified.

A confusion matrix summarizes the performance of a classification algorithm by showing the true positives, false positives, true negatives, and false negatives.

One-hot encoding converts categorical variables into a numerical format by creating binary columns for each category, allowing algorithms to process them effectively.

Normalization scales features to a similar range, ensuring that all features contribute equally to distance calculations in algorithms like K-Means.

Neural networks can predict player behavior, allowing for more adaptive and engaging gaming experiences.

Heteroscedasticity refers to the condition where the variance of the errors is not constant across all levels of the independent variable.

Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data.

A residual is the difference between the observed value of the dependent variable and the value predicted by the regression model.

Homoscedasticity refers to the condition where the variance of the residuals is constant across all levels of the independent variable.

Overfitting occurs when a model learns the training data too well, capturing noise and leading to poor performance on unseen data.

Model drift refers to changes in the underlying data distribution that can lead to a decrease in the model's performance over time.

Scalability refers to the ability of the deployed model to handle increased load or demand.

Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.

Cross-validation helps to reduce overfitting and provides a better assessment of model performance by using different subsets of the data.

Cross-validation helps to prevent overfitting by ensuring that the model performs well on unseen data.

Dropout is a regularization technique used to prevent overfitting by randomly setting a fraction of the neurons to zero during training.

An epoch refers to one complete pass through the entire training dataset during the training process.

Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization.

Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization on new data.

Transfer learning involves taking a pre-trained neural network and fine-tuning it for a different but related task.

R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables in the model.