The agglomerative approach in hierarchical clustering starts with individual data points and merges them into larger clusters based on similarity.

'K' represents the number of clusters that the algorithm will create from the data.

If K is set too high, the model may overfit the data, resulting in too many clusters that do not generalize well.

Poorly chosen initial centroids can lead K-means to converge to a local minimum rather than the global minimum, resulting in suboptimal clustering.

Multicollinearity refers to a situation where independent variables in a regression model are highly correlated with each other.

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

A residual is the difference between the observed value and the predicted value in a regression model.

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

Logistic regression outputs a probability between 0 and 1, which can be thresholded to classify data points into binary categories.

Neural networks are used in natural language processing for tasks such as language translation, enabling machines to understand and convert text from one language to another.

Neural networks are effective in sentiment analysis, where they can determine the emotional tone behind a body of text.

Individual trees in Random Forests are trained on random subsets of the data, which helps to create diversity among the trees.

Individual trees in Random Forests are trained using bootstrapped samples of the dataset, which helps to create diversity among the trees.

Random Forests use a random subset of features for each split, which helps in reducing correlation among trees.

Bagging refers to randomly selecting subsets of data to train each tree, which helps to reduce variance and improve model robustness.

Feature randomness refers to the practice of randomly selecting a subset of features for each tree in the forest, which helps to create diverse models.

Out-of-bag error is an estimate of the model's performance calculated using the data points that were not included in the bootstrap sample for each tree.

Bootstrapping involves creating multiple subsets of the training data by sampling with replacement, which helps in building diverse trees in Random Forests.

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

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

Mean Absolute Error (MAE) is used in regression tasks to quantify the average absolute difference between predicted and actual values.

An agent is the entity that interacts with the environment by taking actions to achieve a goal.

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

The primary goal of regression algorithms in supervised learning is to predict continuous outcomes based on input features.

The training dataset is used to train the model on known outcomes, allowing it to learn the relationship between input features and target labels.

The target variable is the output that the model aims to predict based on the input features.

The training dataset is used to train the model, allowing it to learn patterns and relationships in the data.

The training set is used to train the model by providing it with labeled data, allowing it to learn the relationship between inputs and outputs.

Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position of the hyperplane.

Support vectors are the data points that lie closest to the decision boundary and are critical in defining the position and orientation of the boundary.