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Heteroscedasticity
Posted inMachine Learning

Heteroscedasticity in Logistic Regression

In simple terms, homoscedasticity means that the spread (or variance) of the residuals (the differences between observed and predicted values) remains constant across all levels of the predicted values. In…
Posted by tkhan.kiit@gmail.com November 24, 2024
Classification Metrics in Machine Learning
Posted inMachine Learning

Classification Metrics in Machine Learning

Introduction Choosing the right Classification Metrics is very crucial for model evaluation. Metrics like Confusion Matrix is a simple yet a very powerful Classification Metrics when it comes to evaluating…
Posted by tkhan.kiit@gmail.com October 11, 2020
Bias and Variance in Machine Learning
Posted inMachine Learning

Bias and Variance in Machine Learning

Introduction to Bias and Variance Bias and Variance plays a very important role while building a model. To frame it in simple terms Bias is interpreted as the model error…
Posted by tkhan.kiit@gmail.com July 12, 2020
Recent Blogs
  • Logistic Regression : Practical Example
  • Cooks Distance – Absence of Outliers or Influential Points
  • Heteroscedasticity in Logistic Regression
  • End to End Machine Learning Project – Part 2
  • End to End Machine Learning Project – Part 1
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  • Logistic Regression : Practical Example
  • Cooks Distance – Absence of Outliers or Influential Points
  • Heteroscedasticity in Logistic Regression
  • End to End Machine Learning Project – Part 2
  • End to End Machine Learning Project – Part 1
  • Feature Scaling
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