Tag: Regression

Linear regression is often considered the workhorse of predictive modeling, yet its application extends beyond straightforward predictive tasks. This article seeks to enrich the dialogue around regression techniques by introducing Probit Linear Regression as an effective tool for modeling preference...

Logistic regression is one of the most common machine learning algorithms. It can be used to predict the probability of an event occurring, such as whether an incoming email is spam or not, or whether a tumor is malignant or not, based on a given labeled data set. Due to its simplicity, logistic ...

In this article we will explore why we need Logistic, how we derived Logistic from Linear and a few more important facts in mathematics. Let’s Recall the nature of Linear Regression: In Linear Regression, we try to estimate the continuous variable Y depending on any type of variable X wh...

Boosted Regression Trees (from now on BRTs) is a kind of regression methodology based on Machine Learning. Unlike conventional regression methods (GLMs, GAMs), BRTs combine numerous basic decision trees to enhance the predictive performance. BRTs can handle complex relationships and interaction...

As you may notice, it’s a quadratic equation. So, if we substitute 2 with a, 7 with b and 3 with c, we may use the well-known formula to find all the possible values of x : Again, it‘s easy to find the answers: 3 and 0.5....

At its heart, the difference between linear regression and logistic regression lies in the nature of the dependent variable they are each designed to predict. Linear regression is used to forecast a continuous dependent variable using a linear relationship with one or more independent variables. Con...

Regression is a common tool in statistics to test and quantify relationships between variables. The two most common regressions are linear and logistic regressions. A linear regression is used when the dependent variable is quantitative, whereas a logistic regression is u...

Then we rotate the line a little bit and do the same. The line with the smallest sum of squared residuals is the line chosen to fit best. Why can’t we make use of least-squares to find the best fitting line in logistic regression? Well, to answer this we need to recall logistic regressio...

Simply put, simple linear regression is a method used to identify and quantify the relationship between two variables. It allows us to predict the value of one variable based on the value of another. Before we dive in, let’s talk about the assumptions that must be met for simple linear regr...

Ridge Regression Ridge regression is a type of regularized regression model. This means it is a variation of the standard linear regression model that includes a regularized term in the cost function. The purpose of this is to prevent Overfitting. Ridge Regression adds an L2 regularization term t...

The earlier line of code we’re missing here is import statsmodels.formula.api as smf So what we’re doing here is using the supplied ols() or Ordinary Least Squares function from the statsmodels library. OLS is a common technique used in analyzing linear regressio...

Neural network regression is a machine learning technique used for solving regression problems. In regression tasks, the goal is to predict a continuous numeric value (e.g., a price, a temperature, a score) based on input data. Neural networks, a type of deep learning model, can be used for regressi...