Tag Archives: machine learning

Linear Regression Cost Function: Python Example

Cost function in linear regression

Linear regression is a foundational algorithm in machine learning and statistics, used for predicting numerical values based on input data. Understanding the cost function in linear regression is crucial for grasping how these models are trained and optimized. In this blog, we will understand different aspects of cost function used in linear regression including how it does help in building a regression model having high performance. What is a Cost Function in Linear Regression? In linear regression, the cost function quantifies the error between predicted values and actual data points. It is a measure of how far off a linear model’s predictions are from the actual values. The most commonly …

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KNN vs Logistic Regression: Differences, Examples

Difference between K-Nearest Neighbors (KNN) and Logistic Regression algorithms

In this blog, we will learn about the differences between K-Nearest Neighbors (KNN) and Logistic Regression, two pivotal algorithms in machine learning, with the help of examples. The goal is to understand the intricacies of KNN’s instance-based learning and Logistic Regression‘s probability modeling for binary and multinomial outcomes, offering clarity on their core principles. We will also navigate through the practical applications of K-NN and logistic regression algorithms, showcasing real-world examples in various business domains like healthcare and finance. Accompanying this, we’ll provide concise Python code samples, guiding you through implementing these algorithms with datasets. This dual focus on theory and practicality aims to equip you with both the understanding …

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Linear Regression vs Logistic Regression: Differences

simple linear regression model 1

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 …

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Posted in Data Science, Machine Learning, statistics. Tagged with , .

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 …

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F-statistics in Linear Regression: Formula, Examples

linear regression R-squared concepts

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 …

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Python – Replace Missing Values with Mean, Median & Mode

Boxplot for deciding whether to use mean, mode or median for imputation

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 …

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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 …

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AIC in Logistic Regression: Formula, Example

Model evaluation using AIC in Logistic Regression

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 …

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Posted in Data Science, Machine Learning, Python, R. Tagged with , .

30+ Logistic Regression Interview Questions & Answers

machine learning interview questions

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 …

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Difference: Binary vs Multiclass vs Multilabel Classification

Multilayer classifier to tag image with cat, dog, rooster and a donkey

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. …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , .

Classification Problems in Machine Learning: Examples

classification problems real life 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 …

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Mean Squared Error or R-Squared – Which one to use?

Mean Squared Error Representation

Last updated: 27th Nov, 2023 As you embark on your journey to understand and evaluate the performance of regression models, it’s crucial to know when to use each of these metrics and what they reveal about your model’s accuracy. In this post, you will learn about the concepts of the mean-squared error (MSE) and R-squared (R2), the difference between them, and which one to use when evaluating the linear regression models. Note that MSE is very closely related to root mean squared error (RMSE) which is also discussed in this blog. You also learn Python examples to understand the concepts in a better manner. For learning the differences between other …

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Gradient Descent in Machine Learning: Python Examples

Last updated: 26th Nov, 2023 In this post, you will learn about gradient descent algorithm and its importance in training machine learning models. For a data scientist, it is of utmost importance to get a good grasp on the concepts of  gradient descent algorithm as it is widely used for optimizing / minimizing the objective function / loss function related to various machine learning models such as  regression, neural network etc.  in terms of learning optimal weights / parameters. This algorithm is essential because it underpins many machine learning models, enabling them to learn from data by optimizing their performance. By understanding gradient descent, one gains insight into how algorithms …

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Learning Curves Python Sklearn Example

Learning curve explained with python example

Last updated: 26th Nov, 2023 In this post, you will learn about how to use learning curves to assess the improvement in learning performance (accuracy, error rate, etc.) of a machine learning model while implementing using Python (Sklearn) packages. Knowing how to use learning curves will help you assess/diagnose whether the model is suffering from high bias (underfitting) or high variance (overfitting) and whether increasing training data samples could help solve the bias or variance problem. You may want to check some of the following posts in order to get a better understanding of bias-variance and underfitting-overfitting. Bias-variance concepts and interview questions Overfitting/Underfitting concepts and interview questions What are learning curves? …

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Procurement Analytics Use Cases Examples

procurement analytics use cases

Last updated: 26th Nov, 2023 The procurement analytics applications is seeing tremendous growth in last few years. With so much data available, advancement in data analytics and related technology field, and the need for digital transformation across procurement organizations, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases examples from advanced analytics field such as machine learning, AI, generative AI that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts/product managers, and data scientists to implement different use cases using machine learning. The use cases around data-driven decision …

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XGBoost Classifier Explained with Python Example

XGBoost Classification Algorithm

Among the myriad of machine learning algorithms and techniques available with data scientists, one stands out for its exceptional performance in classification problems: XGBoost, short for eXtreme Gradient Boosting. This algorithm has established itself as a force to reckon with in the data science community, as evidenced by its frequent use and high placements in Kaggle competitions, a platform where data scientists and machine learning practitioners worldwide compete to solve complex data problems. The following plot is taken from Francois Chollet tweet. Above demonstrates the prominence of XGBoost as one of the primary machine learning software tools used by the top-5 teams across 120 Kaggle competitions. The data points in …

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