he term “Receiver Operating Characteristic” (ROC) originated in the field of signal detection theory during World War II. Initially, it was used to analyze and measure the performance of radar receivers. The ROC curve, in Machine Learning, is a graphical representation that illustrates the trade-off between true positive rate (sensitivity) and false positive rate (1…
uppose we have a binary classification problem in which we have to predict two classes: 1 and 0. A machine learning model tends to make some mistakes by incorrectly classifying data points, resulting in a difference between the actual and predicted class of the data point. Four possible scenarios that can happen are: Clearly, we want…
In Python, the warnings module provides a way to handle warnings emitted by the Python interpreter or third-party libraries. When you use import warnings, you can control how warnings are displayed or handle them programmatically. Here are some common use cases:
very time you add an independent variable to a model, the R-squared increases, even if the independent variable is insignificant. It never declines. Whereas Adjusted R-squared increases only when independent variable is significant and affects dependent variable. where: Example 1: Calculate Adjusted R-Squared with sklearn Example 2: Calculate Adjusted R-Squared with statsmodels A sample function to…
SequentialFeatureSelector is a feature selection technique. It is part of the feature_selection module and is used for selecting a subset of features from the original feature set. This technique follows a forward or backward sequential selection strategy. Here’s a brief overview: SequentialFeatureSelector is often used in conjunction with machine learning models to identify the most…
One-hot encoding is a technique used in machine learning and data preprocessing to represent categorical variables as binary vectors. In one-hot encoding, each category or label in a categorical variable is represented as a binary vector, where each element corresponds to a unique category. The process involves the following steps: For example, consider a dataset…
Model coefficients, also known as regression coefficients or weights, are the values assigned to the features (independent variables) in a regression model. In a linear regression model, the relationship between the input features (X) and the predicted output (y) is represented as: Here: The model coefficients are estimated during the training of the regression model.…
PolynomialFeatures is a preprocessing technique used in machine learning, particularly in polynomial regression. It transforms an input feature matrix by adding new features that are polynomial combinations of the original features. For example, if you have a feature (x), PolynomialFeatures can generate additional features like , etc., up to a specified degree. This allows the…
uniform distribution is a probability distribution in which all outcomes or events are equally likely to occur. In other words, every possible outcome has the same probability of occurring. In Python, you can use the numpy library to generate random numbers following a uniform distribution. For example:
he binomial distribution is a discrete probability distribution that describes the number of successes in a fixed number of independent Bernoulli trials, each with the same probability of success. In other words, it models the number of successes (e.g., heads in a series of coin flips) in a fixed number of independent experiments, where each…
