Rohan's Bytes

ML Interview Q Series: Estimating True Classifier Accuracy Using Confidence Intervals Based on Test Set Performance.

ML Interview Q Series: Time on Site & Purchases: Establishing Causality with A/B Testing and Confounder Analysis.

ML Interview Q Series: Estimating Exponential Distribution Rate Parameter using Maximum Likelihood

ML Interview Q Series: Navigating Non-i.i.d. Data: Statistical Techniques for Time-Series and Grouped Data Challenges.

ML Interview Q Series: Hypothesis Testing for ML Classification: Navigating Type I and Type II Errors.

ML Interview Q Series: Decoding P-values: Accurate Interpretation in Hypothesis Testing and A/B Experiments.

ML Interview Q Series: Prior vs. Posterior: Understanding Bayesian Belief Updating with Data

ML Interview Q Series: Zero Correlation vs. Independence: Detecting Hidden Non-Linear Dependencies.

ML Interview Q Series: Kullback–Leibler Divergence: Measuring Distribution Differences in Machine Learning.

ML Interview Q Series: Validating Model Accuracy Gains: Statistical Significance Testing for Comparing Classifiers

ML Interview Q Series: Central Limit Theorem: Normality from Averages and Its Importance for Machine Learning Inference.

ML Interview Q Series: Calculating True Disease Probability After Positive Tests Using Bayes' Theorem