Locally Weighted Linear Regression is a non−parametric method/algorithm. In Linear regression, the data should be distributed linearly whereas Locally Weighted Regression is suitable for non−linearly distributed data. Generally, in Locally Weighted Regression, points closer to the query point are given more weightage than points away from it. Parametric and Non-Parametric Models Parametric Parametric models are those which simplify the function to a known form. It has a collection of parameters that summarize the data through these parameters. These parameters are fixed in number, which means that the model already knows about these parameters and they do not depend on the ... Read More

Introduction Handwritten Digit Recognition is a part of image recognition widely used in Computer Vision in Deep learning. Image recognition is one of the very basic and preliminary stages of every image or video−related task in Deep Learning. This article lets an overview of Handwritten Digit Recognition and how Image recognition can be extended to multiclass classification. Before going ahead let us understand the difference between Binary and Multiclass image classification Binary Image Classification In Binary image classification, the model has two classes to predict from. For example in the classification of cats and dogs. Multiclass Image Classification In Multiclass ... Read More

Before getting into the docker container arguments we must know about python command line arguments and how they are accessed by the developer. Command line arguments are of great use when we want our python script to be controlled outside of the program. Access the python script’s command line arguments Step 1: Create a python script main.py Example # sys will allow us to access the passed arguments import sys # sys.argv[0] access the first argument passed that is the python script name print("File or Script Name is :", sys.argv[0]) # print arguments other than the file name ... Read More

Welch's ANOVA, is an expansion of the standard ANOVA test that allows for different sample sizes and variances. Frequently, the samples that are being compared in an ANOVA test may not have comparable variances or sample sizes. In certain situations, Welch's ANOVA should be performed rather than the standard ANOVA test since it can not be acceptable. In this post, we'll take a detailed look at Welch's ANOVA What is Welch’s ANOVA? Welch's ANOVA is a variant of the ANOVA test, which is used to compare the means of two or more samples. ANOVA determines if the means of two ... Read More

ANCOVA (analysis of covariance) is a useful statistical method because it enables the inclusion of covariates in the analysis, which may assist adjust for auxiliary variables and increase the precision of group comparisons. These additional factors, or covariates, may be incorporated into the study using ANCOVA. In order to be sure that any observed differences between the groups are caused by the therapy or intervention under study and not by unrelated factors, ANCOVA can be used to adjust for the impact of the covariates on the group means. This can make the comparisons between the groups more accurate and give ... Read More

Obtaining a p−value from a z−score is a typical statistical procedure. The number of standard deviations a value is from the mean of a normal distribution is expressed as a z−score, sometimes referred to as a standard score. The z-score can be used to assess the probability that a specific value will appear in a normal distribution. The probability of getting a test statistic at least as severe as the one that was observed is the p-value, assuming that the null hypothesis is true. Because the z−score is typically the test statistic, determining the p-value from the z−score allows one ... Read More

Studentized residuals are typically used in regression analysis to identify potential outliers in the data. An outlier is a point that is significantly different from the overall trend of the data, and it can have a significant influence on the fitted model. By identifying and analyzing outliers, you can better understand the underlying patterns in your data and improve the accuracy of your model. In this post, we will be closely looking at Studentized Residuals and how you can implement it in python. What are Studentized Residuals? The term "studentized residuals" refers to a particular class of residuals that have ... Read More

Dunn's test is a statistical technique for comparing the means of several samples. When it's required to compare the means of numerous samples to identify which ones are noticeably different from one another, Dunn's test is frequently employed in a range of disciplines, including biology, psychology, and education. We shall examine Dunn's test in−depth in this article, along with a python implementation. What is Dunn’s Test? Dunn's test is a statistical analysis used to compare the means of numerous samples. It is a form of multiple comparison test used to compare the means of more than two samples to identify ... Read More

Many statistical tests and procedures presume that the data is normal and has equal variances. These criteria frequently determine whether a researcher can apply a parametric or non-parametric test, frame hypotheses in specific ways, and so on. Bartlett's test is a prominent inferential statistics test that deals with data from a normal distribution. This post will show you how to run Bartlett's test in python. What is Bartlett’s test?  Bartlett's test is a statistical test that determines whether or not samples have equal variances. It is a hypothesis test that analyzes the variances of two or more samples to see ... Read More

Introduction As the name implies, machine learning is the practice of creating computer algorithms that can learn from various types of data. In a broader definition, Arthur Samuel states that "machine learning is the scientific field which offers computers the power to learn and without being expressly taught." They are frequently used to deal with a variety of everyday difficulties. The algorithms, mathematical equations, and statistical calculations used in the past to perform Machine Learning tasks were all manually coded. As a result, the procedure was time-consuming, challenging, and ineffective. But because of a number of libraries, frameworks, and modules, ... Read More