Linear regression can be used to forecast sales based on advertising spend, predicting continuous outcomes.

Recurrent Neural Networks (RNNs) are commonly used in natural language processing due to their ability to handle sequential data.

The number of bedrooms is a relevant feature that can significantly impact house prices in a predictive model.

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

A high R-squared with high MSE suggests that while the model explains a lot of variance, it may be overfitting the training data and not generalizing well.

A high R-squared value with poor performance on new data suggests that the model may be overfitting, capturing noise rather than the underlying trend.

High precision and low recall indicate that the model is good at identifying positive cases but fails to capture many actual positives.

The F1 score is calculated as 2 * (precision * recall) / (precision + recall), which results in 0.72.

Classifying emails as spam or not spam is an example of supervised learning, where the model is trained on labeled data.

Cross-validation helps assess how well the feature engineering has improved model performance.

All methods can be valid depending on the context and the amount of missing data.

Return on Investment (ROI) is often used to evaluate the success of a deployed model, as it measures the financial benefits gained from the model compared to its costs.

A confusion matrix represents the performance of a classification model by showing the true positive, true negative, false positive, and false negative counts.

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

The Silhouette Score is commonly used to evaluate the quality of clusters by measuring how similar an object is to its own cluster compared to other clusters.

Customer segmentation in marketing is a common application of clustering to identify distinct customer groups.

Specificity is the True Negative Rate, indicating the proportion of actual negatives that are correctly identified.

Gini impurity measures the likelihood of misclassifying a randomly chosen element from the dataset, helping to determine the best splits.

Gini impurity is a criterion used to measure the impurity of a node, helping to determine the best feature to split on.

A 'node' in a Decision Tree is where a decision is made based on the feature values.

Domain knowledge helps identify relevant features that can significantly impact model performance.

In K-means clustering, the number of centroids initialized is equal to the number of clusters. Therefore, if there are 5 clusters, 5 centroids will be initialized.

Multicollinearity refers to a situation where independent variables in a regression model are highly correlated with each other, which can affect the model's estimates.

A negative coefficient indicates that as the independent variable increases, the dependent variable tends to decrease.

The slope coefficient indicates how much the dependent variable is expected to increase (or decrease) when the independent variable increases by one unit.

The slope indicates how much the dependent variable is expected to increase or decrease as the independent variable increases by one unit.

Macro F1 Score calculates the F1 Score for each class independently and then takes the average, providing a balanced evaluation across all classes.

Micro F1 Score aggregates the contributions of all classes to compute the average metric, suitable for multi-class evaluation.

The activation function determines the output of a neuron based on its input, playing a crucial role in the network's ability to learn complex patterns.

Backpropagation is the algorithm used to update the weights of the network by calculating the gradient of the loss function.