In this blog, I wanna shoot out the exact meaning of CLT with a clear diagrammatic explanation.

The Central Limit Theorem (CLT) is a statistical concept states that the average of your sample mean will be the population mean.

In other words, When you add up the means of all your samples and find the average of it, then that average mean will be very closer or equal to the population mean.


This blog is all about how to push large files on GitHub

Initially, when you Drag files to add them to your repository or choose your file more than 25 Mb you will be notified that

“Yowza, that’s a big file. Try again with a file smaller than 25MB.”!!!!!

I will help you out to solve this issue with few steps.

Step: 1

First, Delete the repository which you have created now

Step: 2

Download 2 files in your system as the link provided below

1 git: click on the Link (Download the Latest source Release for windows)

2 Git Large File Storage (LFS)…


BIAS

Bias is known as the difference between the actual value and predicted value at the time of training. Being high in biasing gives a large error in training as well as testing data. It’s recommended that an algorithm should always be low biased to avoid the problem of underfitting.

VARIANCE

Variance is the measure of the variability in the results predicted by our model so, to put this in a simple way variance quantifies the difference in prediction when we change our dataset. So, when we have high variance, it means that our predictions are going to be very different when…


ENSEMBLE TECHNIQUES

Before we start with Random Forest, we need to be clear with the method and its types of Ensemble method.

Ensemble method

The Ensemble method is a technique that creates multiple models (with a random subset of rows with overlapping)and then combines various models into one effective model. The Ensemble method usually provides more accurate solutions than a single model would.

The Ensemble technique is generally categorized into two types Bagging and Boosting. Here in this blog we extremely focus on Bagging techniques.

Lucky Bachawala

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