Logistic Regression Part I — Transformation of Linear to Logistic
Logistic Regression - Transformation of Linear to Logistic - Explained with images and examples - Sigmoid Function
Category Machine Learning
Underfitted — Generalized — Overfitted
Underfitted Generalized Overfitted, A brief note on how bias and variance makes a model as Underfitted or Generalized or Overfitted
Overfitting — Bias — Variance — Regularization
Overfitting Bias Variance Regularization, Shrinkage/Regularization methods, What is Bias, How to overcome Underfitting and Overfitting
Linear Regression — Part IV — Chance of Admission Prediction
Ever thought about doing magic or predict future? Here is the guide! lol! In this article lets go programming with sklearn package to explore Linear Regression and learn how to do prediction with Linear Regression. We have seen enough theories…
Linear Regression — Part III — R Squared
R Squared is one of the metrics by which we can find the accuracy of a model that we create. R squared metrics works only if the regression model is linear. SSE — Sum of Squares of Residuals (Errors) SSR is the sum…
Linear Regression — Part II — Gradient Descent
Gradient descent is an optimization algorithm used to minimize a cost function (i.e. Error) parameterized by a model. We know that Gradient means the slope of a surface or a line. This algorithm involves calculations with slope. To understand about Gradient Descent, we…
Linear Regression — Part I
Linear Regression is a linear approach to model the relationship between a two or more variables by fitting a straight line i.e. linear, to predict the output for the given input data. To perform Linear Regression, Data should accomplish the below…
What is Regression in Terms of ML?
What is Regression in terms of ML, Regression in Machine Learning, Detailed explanation of Regression and its advantages