## Heap Sort

Heap sort is one of the fastest sorting algorithms. It uses binary heap data structure and its time complexity is O(Log n). In this sorting algorithm, we first build a heap using the given elements. If we want to sort the elements in ascending order, we create a Min Heap. Read more…

## Two Sample Tests- Hypothesis Testing

Till now, we discussed about the one sample z test,t test and p test. We are going to discuss about the two sample z test, p test, t test and paired t test.Before talking on two sample z test, let us understand the difference between one sample z test and Read more…

## One Sample Test- Hypothesis Testing 2

Earlier we discussed about the Hypothesis Testing , what is one sample and two sample tests. Also we have gone through one sample t and z test. Here we are going to discuss p value and one sample p test. What is p value ? p value is the lowest Read more…

## One Sample Test-Hypothesis Testing 1

A one tail is appropriate if the estimated value may depart from the reference value in only one direction. Example : Whether machine is producing more than two percent defective products or less than two percent defective products. There are two tests in one sample test. One is Lower tail Read more…

## Hypothesis Testing

Introduction: Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter. Terms using in Hypothesis Testing : Null Hypothesis: It shows that there is no difference between the means of two Read more…

## Overfit and Underfit Model

Introduction The cause of poor performance in machine learning model is either overfitting or underfitting the data.Gaining a proper understanding of underfitting and overfitting would help us to develop the most accurate model. So let’s start discussing about underfit ,overfit of model and see how they make impacted. What is Read more…

## Feature Scaling

Introduction of Feature Scaling It is a technique, used to normalise the range of independent variables or features of data. Feature scaling means adjusting data that has different scales so as to avoid biases. It is generally performed during the data preprocessing step. It is also known as data normalization. Read more…

Introduction All the models involves significant measures of error. Error can be reducible and non-reducible.The reducible errors which are the bias and variance can be made use off. Gaining a proper understanding of bias, variance, underfitting, overfitting and tradeoff would help us to develop the most accurate model. So let’s Read more…

## K-Fold Cross Validation

The problem with machine learning models is that we don’t know how is model performing until we test its performance on an independent data set, the data set which was not used for training the machine learning model. We need some kind of assurance that our model is low on Read more…

Insert math as
$${}$$