# Tag Archives: Data Science

## Linear Regression vs Logistic Regression: Differences

Last updated: 1st Dec, 2023 In the ever-evolving landscape of machine learning, two algorithms stand out for their simplicity and effectiveness: Linear Regression and Logistic Regression. But what exactly are these algorithms, and how do they differ from each other? At first glance, logistic regression and linear regression might seem very similar – after all, they share the word “regression.” However, the devil, as they say, is in the details. Each method is uniquely tailored to solve specific types of problems, and understanding these subtleties is key to unlocking their full potential. Linear regression and logistic regression are both machine learning algorithms used for modeling relationships between variables but perform …

## Python – How to Create Scatter Plot with IRIS Dataset

Last updated: 1st Dec, 2023 In this blog post, we will be learning how to create a Scatter Plot with the IRIS dataset using Python. The IRIS dataset is a collection of data that is used to demonstrate the properties of various statistical models. It contains information about 50 observations on four different variables: Petal Length, Petal Width, Sepal Length, and Sepal Width. As data scientists, it is important for us to be able to visualize the data that we are working with. Scatter plots are a great way to do this because they show the relationship between two variables. In this post, we learn how to plot IRIS dataset …

## F-statistics in Linear Regression: Formula, Examples

Last updated: 1st Dec, 2023 In this blog post, we will take a look at the concepts and formula of f-statistics in linear regression models and understand how to interpret f-statistics in regression with the help of examples. F-test and related F-statistics interpretation is key if you want to be able to evaluate the regression models based on the summary results of training the model. We will start by discussing the importance of f-statistics in linear regression models and understand how they are calculated based on the f-statistics formula. We will, then, understand the concept with some real-world examples. As data scientists, it is very important to understand both the f-statistics …

## Python – Replace Missing Values with Mean, Median & Mode

Last updated: 1st Dec, 2023 Have you found yourself asking question such as how to deal with missing values in data analysis stage? When working with Python, have you been troubled with question such as how to replace missing values in Pandas data frame? Well, missing values are common in dealing with real-world problems when the data is aggregated over long time stretches from disparate sources, and reliable machine learning modeling demands for careful handling of missing data. One strategy is imputing the missing values, and a wide variety of algorithms exist spanning simple interpolation (mean, median, mode), matrix factorization methods like SVD, statistical models like Kalman filters, and deep …

## Accuracy, Precision, Recall & F1-Score – Python Examples

Last updated: 30th Nov, 2023 Classification models are used in classification problems to predict the target class of the data sample. The classification machine learning models predicts the probability that each instance belongs to one class or another. It is important to evaluate the performance of the classifications model in order to reliably use these models in production for solving real-world problems. The performance metrics include accuracy, precision, recall, and F1-score. Because it helps us understand the strengths and limitations of these models when making predictions in new situations, model performance is essential for machine learning. The most common question asked is what is accuracy, precision, recall and f1 score? In …

## Chebyshev’s Theorem: Formula & Examples

Chebyshev’s theorem is a fundamental concept in statistics that allows us to determine the probability of data values falling within a certain range defined by mean and standard deviation. This theorem makes it possible to calculate the probability of a given dataset being within K standard deviations away from the mean. It is important for data scientists, statisticians, and analysts to understand this theorem as it can be used to assess the spread of data points around a mean value. What is Chebyshev’s Theorem? Chebyshev’s Theorem, also known as Chebyshev’s Rule, states that in any probability distribution, the proportion of outcomes that lie within k standard deviations from the mean …

## Independent Samples T-test: Formula & Examples

Last updated: 30th Nov, 2023 As a data scientist, you may often come across scenarios where you need to compare the means of two independent samples. In such cases, a two independent samples t-test, also known as unpaired two samples t-test, is an essential statistical tool that can help you draw meaningful conclusions from your data. This test allows you to determine whether the difference between the means of two independent samples is statistically significant or due to chance. In this blog, we will cover the concept of independent samples t-test, its formula, real-world examples of its applications and the Python & Excel example (using scipy.stats.ttest_ind function). We will begin …

## AIC in Logistic Regression: Formula, Example

Have you as a data scientist ever been challenged by choosing the best logistic regression model for your data? As we all know, the difference between a good and the best model while training machine learning model can be subtle yet impactful. Whether it’s predicting the likelihood of an event occurring or classifying data into distinct categories, logistic regression provides a robust framework for analysts and researchers. However, the true power of logistic regression is harnessed not just by building models, but also by selecting the right model. This is where the Akaike Information Criterion (AIC) comes into play. In this blog, we’ll delve into different aspects of AIC, decode …

## Linear Regression T-test: Formula, Example

Last updated: 29th Nov, 2023 Linear regression is a popular statistical method used to model the relationship between a dependent variable and one or more independent variables. In linear regression, the t-test is a statistical hypothesis testing technique that is used to test the hypothesis related to linearity of the relationship between the response variable and different predictor variables. In this blog, we will discuss linear regression and t-test and related formulas and examples. For a detailed read on linear regression, check out my related blog – Linear regression explained with real-life examples. T-tests are used in linear regression to determine if a particular variable is statistically significant in the …

## 30+ Logistic Regression Interview Questions & Answers

Last updated: 29th Nov, 2023 This page lists down the practice tests / interview questions and answers for Logistic regression in machine learning. Those wanting to test their machine learning knowledge in relation with logistic regression would find these practice tests very useful. The goal for these practice tests is to help you check your knowledge in logistic regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful. These test primarily focus on …

## Standard Deviation vs Standard Error: Formula, Examples

Understanding the differences between standard deviation and standard error is crucial for anyone involved in statistical analysis or research. These concepts, while related, serve different purposes in the realm of statistics. In this blog, we will delve into their differences, applications in research, formulas, and practical examples. Introduction to Standard Deviation & Standard Error At the heart of statistical analysis lies the need to understand and quantify variability. This is where standard deviation and standard error come into play. What’s standard deviation? Standard Deviation is a measure that reflects the amount of variation or dispersion within a dataset. It indicates how much individual data points deviate from the mean (average) …

## Coefficient of Variation vs Standard Deviation

Last updated: 28th Nov, 2023 Understanding the difference between coefficient of variation (CV) and standard deviation (SD) is essential for statisticians and data scientists. While both concepts measure variability in a dataset, they are calculated differently and can be used in different scenarios for better understanding. Here, we will explore the coefficient of variation vs standard deviation differences to gain a better understanding of how to use them. Coefficient of Variation vs Standard Deviation Coefficient of Variation (CV) is a measure that is used to compare the amount of variation in a dataset relative to its mean value. It is calculated by taking the standard deviation divided by the mean, then …

## Difference: Binary vs Multiclass vs Multilabel Classification

Last updated: 28th Nov, 2023 There are three main types of classification algorithms when dealing with machine learning classification problems: Binary, Multiclass, and Multilabel. In this blog post, we will discuss the differences between them and how they can be used to solve different problems. Binary classifiers can only classify data into two categories, while multiclass classifiers can classify data into more than two categories. Multilabel classifiers assign or tag the data to zero or more categories. Let’s take a closer look at each type! Binary classification & examples Binary classification is a type of supervised machine learning problem that requires classifying data into two mutually exclusive groups or categories. …

## Classification Problems in Machine Learning: Examples

In this post, you will learn about some popular and most common real-life examples of machine learning (ML) classification problems. For beginner data scientists, these examples of classification problems will prove to be helpful to gain perspectives on real-world problems which can be solved using classification algorithms in machine learning. This post will be updated from time-to-time to include interesting examples which can be solved by training classification models. Before going ahead and looking into examples, let’s understand a little about what is an ML classification problem. You may as well skip this section if you are familiar with the definition of machine learning classification problems & solutions. You may …

## Z-test vs T-test vs Chi-square test: Differences, Examples

In the world of data science, understanding the differences between various statistical tests is crucial for accurate data analysis. Three most popular tests – the Z-test, T-test, and Chi-square test – each serve specific purposes. This blog post will delve into their definitions, types, formulas, appropriate usage scenarios, and the Python/R packages that can be used for their implementation, along with real-world examples. Check out a detailed post on the differences between Z-test vs T-test. Definition: What’s Z-test vs T-test vs Chi-square test? The following represents the definition of each of the tests along with a real-world example: Z-test: The Z-test is a statistical test used to determine if there …

## Z-test vs T-test: Differences, Formula, Examples

Last updated: 27th Nov, 2023 When it comes to statistical tests, z-test and t-test are two of the most commonly used. But what is the difference between z-test and t-test? And when to use z-test vs t-test? In this relation, we also wonder about z-statistics vs t-statistics. And, the question arises around what’s the difference between z-statistics and t-statistics. In this blog post, we will answer all these questions and more! We will start by explaining the difference between z-test and t-test in terms of their formulas. Then we will go over some examples so that you can see how each test is used in practice. As data scientists, it …

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