This article aims to explain different Cross-Validation techniques and how they work.

Contents:

Introduction


This article aims to show python implementation for different Hyperparameter Tuning techniques using the RandomForest model.

Contents:

Importance of Hyper-Parameter Tuning!

  1. The goal of any…


Create a .IPYNB file

→ Installing Libraries

!pip install autocorrect
import sys
!{sys.executable} -m pip install contractions
!pip install zeugma

→ Importing Libraries

import re
# --------------------------------------------------------------
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# --------------------------------------------------------------
import nltk
from nltk.stem…


Photo by Tim Gouw on Unsplash

The big 4 metrics :
* Precision
* Recall
* Accuracy
* F1-Score

But will F1 really going to help?

let’s revise our basics.

Also, check out my article on Calculating Accuracy of an ML Model.

  • The…


Content:

  1. What are Ensemble Methods?
  2. Intuition Behind Ensemble Methods!
  3. Different Ensemble Methods
    * Bagging
    →Intuition behind Bagging
    * Boosting
    →Intuition behind Boosting
    * Stacking
    →Intuition behind Stacking
    * Bucket of models

What are Ensemble Methods?

  • Ensemble methods are techniques that create multiple models and then combine them to produce improved results.
  • This approach allows the production of better predictive performance compared to a single model.
  • Ensemble methods usually produce more accurate solutions than a single model would. This has been the case in many machine learning competitions, where the winning solutions used ensemble methods.

The intuition behind the ensemble:


Content:

  1. What is the need for and Importance of Gaussian Distribution?
    → What is Gaussian Distribution?
    → Need for Normal Distribution?
    → Importance of Normality in Machine Learning!
  2. Need for Data Transformation!!
  3. Importance of Data Distribution Transformation.
  4. Different methods to Transform the Distribution.
    → The ladder of powers.
    → Box-Cox Transformation Method…


Problems with imbalanced data?


What is Decision Tree?

  • A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
  • Decision tree learning is one of the predictive modelling approaches used in statistics and machine learning. It uses a decision tree to go from observations about an item (represented in the branches) to conclusions about the item’s target value (represented in the leaves).
  • Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks.
  • The goal is to create a model that predicts the value of a target variable…


Content:

  1. What is Polynomial Regression?
  2. Assumptions of Polynomial Regression.
  3. Why do we need Polynomial Regression?
  4. How to find the right degree of the Polynomial Equation?
  5. Math Behind Polynomial Equation.
  6. Cost Function of Polynomial Regression.
  7. Polynomial Regression with Gradient Descent.

What is Polynomial Regression?

  • Polynomial Regression is a form of regression analysis in which the relationship between the independent variables and dependent variables are modeled in the nth degree polynomial.
  • Polynomial Regression models are usually fit with the method of least squares.The least square…


Content

  1. What is Logistic Regression?
  2. Types of Logistic Regression.
  3. Assumptions of Logistic Regression.
  4. Why not Linear Regression for Classification?
  5. The Logistic Model.
  6. Interpretation of the co-efficients.
  7. Odds Ratio and Logit
  8. Decision Boundary.
  9. Cost Function of Logistic Regression.
  10. Gradient Descent in Logistic Regression.
  11. Evaluating the Logistic Regression Model.

Let’s get Started

Abhigyan

An electronics and communication engineer with passion towards data science,I write articles for people like me to understand things in laymen terms.

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