The understanding of statitic allows you to well prepare your data, select the right models, and interpret the different outcomes (e.g. coefficients of regressions, odds ratio, measures of quality of models and evaluations)

A robust understanding of statistics greatly enhances machine learning models by aiding in feature selection, data preprocessing, model evaluation, selection, parameter tuning, understanding assumptions, and dealing with uncertainty. Statistics guides the identification of relevant features, handling missing values and outliers, normalization, and interpreting model performance using metrics like accuracy and precision. It also facilitates model comparison and selection through techniques like cross-validation and hypothesis testing while enabling the optimization of model hyperparameters using methods such as grid search. Understanding statistical assumptions underlying machine learning algorithms is essential for choosing appropriate models and interpreting results accurately, and statistical tools like confidence intervals help quantify prediction uncertainty, ensuring more reliable model outcomes.

A strong understanding of statistics is crucial in machine learning for several reasons. It aids in effectively understanding and preparing data, identifying key trends, and handling variability. Statistical techniques are essential for feature selection and engineering, helping to focus on relevant variables and reduce dimensionality. They provide robust methods for model evaluation, including hypothesis testing and validation techniques, ensuring model reliability. Knowledge of statistics is vital in choosing and applying the right machine learning algorithms, as different methods have varying assumptions about data. It also underpins advanced areas like probabilistic modeling and Bayesian approaches in machine learning. Overall, statistics is fundamental in enhancing the accuracy, efficiency, and effectiveness of machine learning models.