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Fundamentals of Machine Learning: A Pythonic Introduction

Parameters

Learn about machine learning models' parameters, hyperparameters, and their importance.

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In the previous lesson, we learned that a model needs features (inputs) and targets (outputs) to perform a task like classification or prediction. Now, we look inside the model to understand how it uses these features to map to a target. This mapping is defined by a parametric model, which is controlled by adjustable values called parameters.

Parametric model

Parametric models are functions defined by a fixed set of parameters(often called weights) in machine learning. These are assumed to be able to approximate the underlying pattern of the data. By adjusting the values of the parameters during the training process, these models can learn to fit the data and make accurate predictions on new inputs.

Consider a simple linear function:

fw1,w2,w0(x1,x2)=w1x1+w2x2+w0f_{w_1,w_2,w_0}(x_1,x_2)=w_1x_1+w_2x_2+w_0

This is an instance of a function class (or model class), which is a family of possible functions defined by the same structure. The variables w1w_1, w2w_2, and w0w_0 are the parameters (or weights). Any specific choice of these parameters (e.g., w1=2,w2=−3,w0=7w_1=2, w_2=-3, w_0=7) results in a specific function instance, f(x1,x2)=2x1−3x2+7f(x_1, x_2) = 2x_1 - 3x_2 + 7, that belongs to this model class.

The goal of training is to find the single best set of parameters (w1,w2,w0w_1, w_2, w_0 ...