Softmax regression is a supervised machine learning algorithm used in classification tasks where a target variable $y$ may take $K$ values, and we are interested in labeling an observation composed of $N$ features/predictors. While workarounds such as OvR and OvO allow to use binary classifiers (e.g. logistic regression) in multinomial...
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## Linear and Quadratic Discriminant Analysis

Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are supervised machine learning algorithms used for multinomial classification tasks. Both LDA and QDA estimate $P(y \;\vert\;\mathbf{x})$ relying on Bayes theorem, i.e., first calculating $P(\mathbf{x} \;\vert\; y)$, and eventually $P(y)$ too. For each class $k \in \{1, \ldots, K\}$, LDA assumes...
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## Maximum Likelihood and Bayesian Estimation

A training set $X$ composed of $m$ samples, i.e., $X = (x^{(1)}, \ldots, x^{(m)})$, can be modeled as the outcome of a random variable $\mathcal{X}$. If we knew how this variable behaves, we would then be able to characterize the dataset. The behavior of a random variable is determined by...
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## Logistic Regression

Logistic regression is a supervised machine learning algorithm used in classification tasks where a target variable $y$ may take $K$ possible values, and we are interested in labeling an observation $\mathbf{x}$ composed of $N$ features/predictors. In particular, logistic regression is used in binary classification tasks, i.e., cases where $K=2$. However,...
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## Linear and Polynomial Regression

Linear and polynomial regression are supervised machine learning algorithms used, as their names indicate, to perform regression tasks, i.e., to estimate the values of a continuous response/target variable $y$.
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