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What is one-hot encoding?

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One-hot encoding is a technique used to convert categorical variables into a binary matrix representation, where each category is represented by a binary vector. For example, if a variable has three categories (A, B, C), it will be represented as three binary columns: [1,0,0], [0,1,0], and [0,0,1].

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What is a multilayer perceptron (MLP)?

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A multilayer perceptron (MLP) is a type of artificial neural network that consists of multiple layers of nodes (neurons), including an input layer, one or more hidden layers, and an output layer. MLPs are used for supervised learning tasks such as classification and regression.

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What is the purpose of regularization in machine learning?

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Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty for larger coefficients in the model. This helps to improve the model's generalization to unseen data.

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What is elastic net regression?

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Elastic net regression is a regularization technique that combines both Lasso and Ridge regression. It adds both L1 and L2 penalties to the loss function, allowing for variable selection and regularization simultaneously.

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What is clustering in machine learning?

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Clustering is an unsupervised learning task that involves grouping a set of objects into clusters based on their similarities. The goal is to ensure that objects in the same cluster are more similar to each other than to those in other clusters.

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What is the K-means algorithm?

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K-means is a popular clustering algorithm that partitions data into K distinct clusters based on feature similarity. It works by initializing K centroids, assigning data points to the nearest centroid, and then updating the centroids based on the assigned points.

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What is the difference between supervised and unsupervised learning?

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Supervised learning involves training a model on labeled data, where the outcome is known, while unsupervised learning involves training a model on unlabeled data, where the outcome is not known.

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What is a confusion matrix?

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A confusion matrix is a table used to evaluate the performance of a classification model. It summarizes the true positives, true negatives, false positives, and false negatives, allowing for the calculation of various performance metrics.

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What is the purpose of data preprocessing?

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Data preprocessing is the process of cleaning and transforming raw data into a format suitable for analysis. It includes steps such as handling missing values, normalizing data, and encoding categorical variables.

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What is feature selection?

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Feature selection is the process of selecting a subset of relevant features (variables) for use in model construction. It helps to improve model performance and reduce overfitting by eliminating irrelevant or redundant features.