Author: Tayyaba Syed
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Classification Algorithms
Classification is a type of supervised learning technique that involves predicting a categorical target variable based on a set of input features. It is commonly used to solve problems such as spam detection, fraud detection, image recognition, sentiment analysis, and many others. The goal of a classification model is to learn a mapping function (f)…
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Polynomial Regression
Polynomial Linear Regression is a type of regression analysis in which the relationship between the independent variable and the dependent variable is modeled as an n-th degree polynomial function. Polynomial regression allows for a more complex relationship between the variables to be captured, beyond the linear relationship in Simple and Multiple Linear Regression. Python Implementation…
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Multiple Linear Regression
It is basically the extension of simple linear regression that predicts a response using two or more features. Mathematically we can explain it as follows − Consider a dataset having n observations, p features i.e. independent variables and y as one response i.e. dependent variable the regression line for p features can be calculated as follows − h(xi)=b0+b1xi1+b2xi2+⋅⋅⋅+bpxiph(xi)=b0+b1xi1+b2xi2+⋅⋅⋅+bpxip Here,h(xi)h(xi) is the predicted…
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Simple Linear Regression
Simple linear regression is a type of regression analysis in which a single independent variable (also known as a predictor variable) is used to predict the dependent variable. In other words, it models the linear relationship between the dependent variable and a single independent variable. Python Implementation Given below is an example that shows how…
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Linear Regression in Machine Learning
Linear regression in machine learning is defined as a statistical model that analyzes the linear relationship between a dependent variable and a given set of independent variables. The linear relationship between variables means that when the value of one or more independent variables will change (increase or decrease), the value of the dependent variable will…
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Regression Analysis in Machine Learning
What is Regression Analysis? In machine learning, regression analysis is a statistical technique that predicts continuous numeric values based on the relationship between independent and dependent variables. The main goal of regression analysis is to plot a line or curve that best fit the data and to estimate how one variable affects another. Regression analysis…
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Hypothesis in Machine Learning
In machine learning, a hypothesis is a proposed explanation or solution for a problem. It is a tentative assumption or idea that can be tested and validated using data. In supervised learning, the hypothesis is the model that the algorithm is trained on to make predictions on unseen data. Hypothesis in machine learning is generally expressed…
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Bias and Variance in Machine Learning
Bias and variance are two important concepts in machine learning that describe the sources of error in a model’s predictions. Bias refers to the error that results from oversimplifying the underlying relationship between the input features and the output variable. At the same time, variance refers to the error that results from being too sensitive to fluctuations in the…
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Skewness and Kurtosis
Skewness and kurtosis are two important measures of the shape of a probability distribution in machine learning. Skewness refers to the degree of asymmetry of a distribution. A distribution is said to be skewed if it is not symmetrical about its mean. Skewness can be positive, indicating that the tail of the distribution is longer…
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Data Distribution
In machine learning, data distribution refers to the way in which data points are distributed or spread out across a dataset. It is important to understand the distribution of data in a dataset, as it can have a significant impact on the performance of machine learning algorithms. Data distribution can be characterized by several statistical…