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In supervised learning, the algorithm is trained on labeled data, meaning the output is known. The goal is to learn a mapping from inputs to outputs. In unsupervised learning, the algorithm is trained on unlabeled data, trying to discover inherent patterns or structures from the data, like clustering or dimensionality reduction.

Common strategies include:

   – Removing rows or columns with missing data.

   – Imputation using the mean, median, or mode.

   – Using algorithms like k-NN or regression to estimate missing values.

   – Using algorithms robust to missing values, like XGBoost.

Bias refers to the error due to overly simplistic assumptions in the learning algorithm. High bias can cause the model to miss relevant relations (underfitting). Variance refers to the error due to excessive complexity in the model. High variance can cause overfitting. The trade-off implies that as you increase a model’s complexity, variance will increase and bias will decrease, and vice versa.

Random Forest is an ensemble method that creates a ‘forest’ of decision trees. During training, each tree is grown from a bootstrap sample of the data, and at each split, a random subset of features is considered. This randomness ensures that the trees are uncorrelated. For predictions, the forest takes an average (regression) or majority vote (classification) of individual trees.

Regularization helps prevent overfitting by adding a penalty to the model complexity. L1 (Lasso) and L2 (Ridge) are common regularization techniques. L1 tends to lead to sparse models where only some features are used, while L2 shrinks the coefficients of less important features towards zero.

MLE is a method used to estimate the parameters of a statistical model. It works by finding the parameter values that maximize the likelihood of making the observed data most probable.

The Receiver Operating Characteristic (ROC) curve plots the true positive rate against the false positive rate for various threshold values, allowing the user to analyze the trade-offs between sensitivities and specificities. The area under the ROC curve (AUC) provides a scalar measure of a model’s performance.

Gradient descent is an optimization algorithm used to minimize the loss function by iteratively moving towards the direction of steepest decrease in loss. The model parameters are updated in the direction of the negative gradient of the loss function.

PCA is a dimensionality reduction technique that transforms features into a new coordinate system by choosing axes (principal components) that maximize variance. The first principal component accounts for the most variance, the second (orthogonal to the first) accounts for the second most, and so on.

Depending on the type of problem:

   – Regression: MSE, RMSE, MAE, R-squared.

   – Classification: Accuracy, Precision, Recall, F1-Score, AUC-ROC.

   – Clustering: Silhouette coefficient, Davies-Bouldin index.

   – Cross-validation should be used to ensure the evaluation is robust and not merely due to a specific data split.

Bagging (Bootstrap Aggregating) involves training multiple instances of the same model on different subsets of data and aggregating the results. It reduces variance and is parallelizable. Boosting, on the other hand, trains models sequentially where each model corrects the errors of its predecessor. It can reduce bias and variance but is inherently sequential.

Activation functions introduce non-linearity to the model, enabling neural networks to learn complex boundaries. Without them, no matter how many layers are added, the network would only be capable of linear transformations.

Both problems pertain to the gradients during backpropagation in deep networks. The vanishing gradient problem occurs when gradients are too small, leading to very slow or no learning. The exploding.