Bias vs Variance, Overfitting vs Underfitting Photo by Gabby K from Pexels Why we need a bias-variance tradeoff In machine learning, we collect data and build
Statistics p-value approach, critical value approach, confidence interval approach Photo by Pixabay from Pexels Hypothesis Testing Hypothesis testing is used to determine whether the assumption about the
Sigmoid function, Log Loss, Odds Ratio, Model coefficient, Metrics Photo by Mikael Blomkvist from Pexels Logistic Regression Logistic Regression is one of the supervised machine learning
Unpack multiple values, ignore some, handle errors, and more Photo by Markus Spiske on Unsplash. Packing means collecting several values into a single variable (tuple), like a=’red’,’square’,’apple’.
Correlation Coefficient, Coefficient of determination, Model Coefficient Photo by Laura James from Pexels Linear Regression Linear Regression is one of the most important algorithms in machine
Drop Values, Fill Values, Replace Values Photo by Gabby K from Pexels Handling Missing Values in Pandas Data Cleaning is one of the important steps in EDA. Data