# Category Archives: Data Science

## CART Decision Tree Python Example

The Classification and Regression Tree (CART) is a supervised machine learning algorithm used for classification, regression. In this blog, we will discuss what CART decision tree is, how it works, and provide a detailed example of its implementation using Python. What is CART & How does it work? CART stands for Classification And Regression Tree. It is a type of decision tree which can be used for both classification and regression tasks based on non-parametric supervised learning method. The following represents the algorithm steps. First and foremost, the data is split into training and test set. Take a feature K and split the training data set into two subsets based on …

## Decision Tree Concepts, Examples, Interview Questions

Decision tree is one of the most commonly used machine learning algorithms which can be used for solving both classification and regression problems. It is very simple to understand and use. Here is a lighter one representing how decision trees and related algorithms (random forest etc) are agile enough for usage. In this post, you will learn about some of the following in relation to machine learning algorithm – decision trees vis-a-vis one of the popular C5.0 algorithm used to build a decision tree for classification. In another post, we shall also be looking at CART methodology for building a decision tree model for classification. The post also presents a …

## Two-way ANOVA Test: Concepts, Formula & Examples

The two-way analysis of variance (ANOVA) test is a powerful tool for analyzing data and uncovering relationships between a dependent variable and two different independent variables. It’s used in fields like psychology, medicine, engineering, business, and other areas that require a deep understanding of how two separate variables interact and impact dependent variable. With the right knowledge, you can use this test to gain valuable insights into your data. Through a two-way ANOVA, data scientists are able to assess complex relationships between multiple variables and draw meaningful conclusions from the data. This helps them make informed decisions and identify patterns in the data that may have gone unnoticed otherwise. Let’s …

## Bias-Variance in Machine Learning: Trade-off, Examples

Bias and variance are two important properties of machine learning models. In this post, you will learn about the concepts of bias & variance in relation to the machine learning (ML) models. Bias refers to how well your model can represent all possible outcomes, whereas variance refers to how sensitive your predictions are to changes in the model’s parameters. The tradeoff between bias and variance is a fundamental problem in machine learning, and it is often necessary to experiment with different model types in order to find the balance that works best for a given dataset. In addition to learning the concepts related to bias vs variance trade-off, you would …

## Population & Samples in Statistics: Examples

In statistics, population and sample are two fundamental concepts that help us to better understand data. A population is a complete set of objects from which we can obtain data. A population can include all people, animals, plants, or things in a given area. On the other hand, a sample is a subset of the population that is used for observation and analysis. In this blog, we will further explore the concepts of population and samples and provide examples to illustrate the differences between them in statistics. What is a population in statistics? In statistics, population refers to the entire set of objects or individuals about which we want to …

## Chebyshev’s Theorem: Concepts, 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. 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 is used to determine the approx. percentage of values that lie within a given number of standard deviations from the mean of a set of data whose shape of distribution …

## Standard Deviation of Population vs Sample

Have you ever wondered what the difference between standard deviation of population and a sample? Or why it’s important to measure the standard deviation of both? In this blog post, we will explore what standard deviation is, the differences between the standard deviation of population and samples, and how to calculate the standard deviation of both using with the help of Python code example. By the end of this post, you should have a better understanding of standard deviation in general and why it’s important to calculate it for both populations and samples. In this post, you will learn about the statistics concepts of standard deviation . What is Standard …

## Account Receivables Use Cases for Machine Learning / AI

Account receivables (AR) account for a significant portion of total assets and revenue. However, the account receivable process is typically handled manually by accountants or finance staff. This can lead to inefficiencies when it comes to identifying account issues and resolving them quickly. In addition, there are opportunities of leveraging data-driven decision making in different areas related to account receivables. In this blog post, you will learn about account receivables analytics use cases and how AI/machine learning and deep learning techniques can be used to streamline account receivable processes. For product managers and data scientists, this post will prove to be useful to understand different machine learning use cases related …

## Bayesian thinking & Real-life Examples

Bayesian thinking is a powerful way of looking at the world, and it can be useful in many real-life situations. Bayesian thinking involves using prior knowledge to make more accurate predictions about future events or outcomes. It is based on the Bayes theorem, which states that the probability of an event occurring is determined by its prior probability combined with new information as it becomes available. It is important for data scientists to learn about Bayesian thinking because it can help them make accurate predictions and draw more meaningful insights from data. In this blog post, we will discuss Bayesian thinking and provide some examples from everyday life to illustrate …

## True Error vs Sample Error: Difference

Understanding the differences between true error and sample error is an important aspect of data science. In this blog post, we will be exploring the difference between these two common features of statistical inference. We’ll discuss what they are and how they differ from each other, as well as provide some examples of real-world scenarios where an understanding of both is important. By the end, you should have a better grasp of the differences between true error and sample error. In case you are a data scientist, you will want to understand the concept behind the true error and sample error. These concepts are key to understand for evaluating a …

## Confidence Intervals Formula, Examples

In this post, you will learn about the statistics concepts of confidence intervals in relation to machine learning models with the help of an example and Python code examples. You will learn about how to interpret confidence intervals, what are formulas for confidence intervals with the help of examples. When you get a hypothesis function by training a machine learning classification model, you evaluate the hypothesis/model by calculating the classification error. The classification error is calculated on the sample of the data used for training the model. However, does this classification error for the sample (sample error) also represent (same as) the classification error of the hypothesis/model for the entire …

## Paired Sample T-Tests: Formula, Examples

Paired sample t-tests are a commonly used statistical procedure used to compare two populations that are related in some way. They are often used for comparing dependent groups, such as the before and after results of an experiment. Data scientists must have a thorough understanding of the concept of paired sample t-test in order to craft accurate and reliable results when analyzing data. In this blog post, we will explore the formula, assumptions, and examples of paired sample t-tests. What is Paired Sample T-Test and Why is it needed? Paired sample t-tests are used to test whether means of same or similar group different from each other under separate conditions …

## Hidden Markov Models: Concepts, Examples

Hidden Markov models (HMMs) are a type of statistical modeling that has been used for several years. They have been applied in different fields such as medicine, computer science, and data science. The Hidden Markov model (HMM) is the foundation of many modern-day data science algorithms. It has been used in data science to make efficient use of observations for successful predictions or decision-making processes. This blog post will cover hidden Markov models with real-world examples and important concepts related to hidden Markov models. What are Markov Models? Markov models are named after Andrey Markov, who first developed them in the early 1900s. Markov models are a type of probabilistic …

## Logistic Regression Concepts, Python Example

In this blog post, we will discuss the logistic regression machine learning algorithm with a python example. Logistic regression is a type of regression algorithm that is used to predict the probability of occurrence of an event. It is often used in machine learning applications. In this tutorial, we will use python to implement logistic regression for binary classification problems. What is Logistic Regression? Logistic regression is a machine learning algorithm used for classification problems. That is, it can be used to predict whether an instance belongs to one class or the other. For example, it could be used to predict whether a person is male or female, based on …

## Types & Uses of Moments in Statistics

In statistics, moments are measures of the shape and variability of a data set. They are used to describe the location and dispersion of the data. There are several types of moments that can be calculated, each providing different information about the data set. Let’s take a look at some of these moments and how they can be used in statistical analysis. What are moments in Statistics and what are their types? In statistics, moments are an important tool used to measure the characteristics of a distribution. Moments can provide useful information about the spread, shape, and center of a distribution. The following are different types of moments: First moment …

## Levene Test & Statistics: Concepts & Examples

The Levene test is used to test for equality of variance in a dataset. It is used in statistical analysis to determine if two or more samples have similar variances. If the results of the test indicate that the samples do not have similar variances, then it means that one sample has a higher variance than the other and should be treated as an outlier. In this blog post, we’ll take a look at what exactly the Levene test is, how it works, and provide some examples of how it can be applied. As data scientists, it will be important for us to understand the Levene test in order to …