Classical ML (Machine Learning)

They are software models that make predictions by extracting statistical patterns over large datasets, learning the rules from data instead of humans.

Classical machine learning does not understand text or context like humans at all. Instead, it makes linear probability calculations by converting the frequencies of words and letters into mathematical weights. For this reason, although it cannot resolve implicit subtle ironies, it can detect certain patterns within its statistical limits.

Example: A model of approximately 7 MB in size running on the device making a prediction just by looking at the word frequencies in the title without reading the body of a news item.

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Machine learning models prevent overfitting with techniques like L2 normalization. Text data; consisting of letters and words, is converted into a space (with TF, IDF formulas) generally having around 16,000 (8,000+8,000) dimensions. Weights are adjusted with optimization methods like L-BFGS. In the stage where the model produces output, the summed word weights pass through a decision function (sigmoid) and are converted into a probability value. This statistical approach is transparent; why the model made that decision can easily be explained by looking at the mathematical contribution of each word.