Logistic Regression – Cost Function, Error Metrics, Precision, Recall, Specificity, ROC Curve, F-Score, Observations from ROC Curve
Logistic Regression – Transformation of Linear to Logistic – Explained with images and examples – Sigmoid Function
In this article, we will see how to join 2 Numpy arrays using built-in funcitons. Numpy – Joining two numpy arrays stack — Joins arrays…
Underfitted Generalized Overfitted, A brief note on how bias and variance makes a model as Underfitted or Generalized or Overfitted
Overfitting Bias Variance Regularization, Shrinkage/Regularization methods, What is Bias, How to overcome Underfitting and Overfitting
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…
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…
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…
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…
Correlation Vs Causation, We know that correlation and causation talks about the relationship between 2 variables.