10 machine learning algorithms to know
In simple terms, a machine learning algorithm is like a recipe that allows computers to learn and make predictions from data. Instead of explicitly telling the computer what to do, we provide it with a large amount of data and let it discover patterns, relationships, and insights on its own.
From classification to regression, here are 10 algorithms you need to know in the field of machine learning:
1. Linear regression
Linear regression is a supervised learning algorithm used for predicting and forecasting values that fall within a continuous range, such as sales numbers or housing prices. It is a technique derived from statistics and is commonly used to establish a relationship between an input variable (X) and an output variable (Y) that can be represented by a straight line.
In simple terms, linear regression takes a set of data points with known input and output values and finds the line that best fits those points. This line, known as the "regression line," serves as a predictive model. By using this line, we can estimate or predict the output value (Y) for a given input value (X).
Linear regression is primarily used for predictive modeling rather than categorization. It is useful when we want to understand how changes in the input variable affect the output variable. By analyzing the slope and intercept of the regression line, we can gain insights into the relationship between the variables and make predictions based on this understanding.
2. Logistic regression
Logistic regression, also known as "logit regression," is a supervised learning algorithm primarily used for binary classification tasks. It is commonly employed when we want to determine whether an input belongs to one class or another, such as deciding whether an image is a cat or not a cat.
Logistic regression predicts the probability that an input can be categorized into a single primary class. However, in practice, it is commonly used to group outputs into two categories: the primary class and not the primary class. To accomplish this, logistic regression creates a threshold or boundary for binary classification. For example, any output value between 0 and 0.49 might be classified as one group, while values between 0.50 and 1.00 would be classified as the other group.
Consequently, logistic regression is typically used for binary categorization rather than predictive modeling. It enables us to assign input data to one of two classes based on the probability estimate and a defined threshold. This makes logistic regression a powerful tool for tasks such as image recognition, spam email detection, or medical diagnosis where we need to categorize data into distinct classes.3