Cross-validation helps ensure that the model is not overfitting by validating it on different subsets of the data.

Cross-validation helps assess how well a model generalizes to an independent dataset.

Cross-validation is used to evaluate the generalization ability of a model by partitioning the data into subsets and training/testing multiple times.

Cross-validation is used to assess how the results of a statistical analysis will generalize to an independent data set, helping to evaluate model performance.

Cross-validation is used to assess how the results of a statistical analysis will generalize to an independent dataset by partitioning the data.

Dropout is a regularization technique used to prevent overfitting by randomly dropping units during training.

Feature importance helps to identify which features have the most influence on the predictions made by the model.

Feature scaling improves the performance of the model by ensuring that all features contribute equally to the distance calculations.

Feature selection aims to select a subset of relevant features to reduce the complexity of the model and improve its performance.

Hyperparameter tuning optimizes the model's parameters to enhance its predictive performance.

Hyperparameter tuning aims to improve model performance by optimizing the parameters that govern the learning process.

Model selection aims to identify the most suitable algorithm that performs best on the given dataset.

Model selection aims to choose the best model from a set of candidates based on performance metrics.

Monitoring a deployed machine learning model is essential to ensure that it maintains accuracy and performance over time as data and conditions change.

Monitoring a deployed model is essential to ensure it continues to perform well and remains accurate over time.

Normalization adjusts the scale of feature values to ensure that they contribute equally to distance calculations, which is crucial for algorithms sensitive to feature scales.

Normalization helps to ensure that all input features contribute equally to the model's learning process by scaling them to a similar range.

One-hot encoding is used to convert categorical variables into a numerical format, allowing them to be used in machine learning algorithms.

Pruning is used to remove unnecessary branches from a Decision Tree to reduce complexity and prevent overfitting.

Regularization techniques, such as Lasso and Ridge regression, are used to reduce the risk of overfitting by penalizing large coefficients.

Regularization is used in regression models to prevent overfitting by adding a penalty for large coefficients, thus simplifying the model.

Regularization techniques are used to prevent overfitting by adding a penalty for larger coefficients in the model.

Bootstrap sampling allows Random Forests to create multiple subsets of the training data, which helps in building diverse trees.

The 'gamma' parameter in the RBF kernel controls the influence of a single training example, affecting the decision boundary's shape.

'min_samples_split' controls the minimum number of samples required to split an internal node, helping to prevent overfitting.

'n_estimators' specifies the number of trees in the Random Forest, which affects the model's performance and stability.

AUC measures the model's performance across all classification thresholds, indicating its ability to distinguish between classes.

AUC-ROC evaluates the trade-off between the true positive rate and false positive rate across different thresholds.

The confusion matrix visualizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.

The elbow method helps identify the optimal number of clusters by plotting the explained variance against the number of clusters.