Statistics for Machine Learning



We consider Statistics to be a prerequisite to the field of applied machine learning. We need statistics to help transform observations into information and to answer questions about samples of observations.

Statistics are important because today we live in the information world and much of this information is determined mathematically by Statistics Help. It means to be informed, correct data, and static concepts are necessary. They use their statistical skills to collect relevant data.

In this article, I will discuss the importance of Statistics to Machine Learning for Data Scientist & Machine Learning Engineer and any other person who sees Data as the new gold.

The following are the highlights of the effects of Statistics on Machine Learning model as a solution

1. Statistics in Data Preparation

They require statistical methods in preparing training and testing data for your machine learning model.

This includes techniques for:

  • Outlier detection.
  • Missing value imputation.
  • Data sampling.
  • Data scaling.
  • Variable encoding.

A basic understanding of data distributions, descriptive statistics, and data visualization is required to help you identify the methods to choose when performing these tasks.

2. Statistics in Model Evaluation

It requires statistical methods when evaluating the skill of a machine learning model on data not seen during training.

This includes techniques for:

  • Data sampling.
  • Data resampling.
  • Experimental design.

Re-sampling techniques such as k-fold cross-validation are often well understood by machine learning practitioners, but the rationale for why this method is required is not.

3. Statistics in Model Selection

They require statistical methods when selecting a final model or model configuration to use for a predictive modeling problem.

These include techniques for:

  • Checking for a significant difference between results.
  • Quantifying the size of the difference between results.

This might include the use of statistical hypothesis tests.

4. Statistics in Model Presentation

It requires statistical methods when presenting the skill of a final model to stakeholders.

This includes techniques for:

  • Summarizing the expected skill of the model on average.
  • Quantifying the expected variability of the skill of the model in practice.

This might include estimation statistics such as confidence intervals.

5. Statistics in Prediction

It requires statistical methods when making a prediction with a finalized model on new data.

This includes techniques for:

  • Quantifying the expected variability for the prediction.

This might include estimation statistics such as prediction intervals.


For a successful Machine Learning life cycle, the above facts about statistics are IMPORTANT to build a productive machine learning solution.

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