Statistics in AI Engineering : Core Concepts Every Machine Learning Engineer Must Know

Statistics is a core part of AI engineering because it helps us understand data, make decisions, and build intelligent systems. In this article, we will explore statistics in AI engineering in the context of machine learning using Python.

What Is Statistics?

Statistics is a key part of AI engineering because it helps us understand what’s going on in our data. It is a branch of mathematics that focuses on collecting, analyzing, interpreting, and presenting data. It allows us to organize information in a clear and useful way to understand what the data tells us. Whether finding the average of a set of numbers or understanding how data points are spread out, statistics provides the basic methods we use to work with data.

In AI, we often work with large amounts of data to train models that can recognize patterns, make predictions, or automate tasks. Statistics gives us the tools to explore and analyze this data in a structured way. It helps us identify what information is useful, detect patterns or trends, and remove errors or noise from the data. 

How Does Statistics Help in AI Engineering?

In machine learning, statistics helps us at many stages. It is used to explore and clean data before building models. It helps us visualize patterns, trends, and relationships that might not be obvious at first glance. Statistics also guides us in choosing the right algorithms and evaluating how well our models perform. Without a strong understanding of statistics, it would be difficult to draw accurate conclusions from data or build reliable machine learning systems.

Basic Statistics Concepts for Machine Learning

The following are some of the important statistics concepts essential for machine learning −

• Mean, Median, Mode − These statistical measures are used to describe the central tendency of a dataset.
• Standard deviation, Variance − Standard deviation is a measure of the amount of variation or dispersion of a set of data values around their mean.
• Percentiles − A percentile is a measure that indicates the value below which a given percentage of observations in a group of observations falls.
• Data Distribution − It refers to how data points are distributed or spread out across a dataset.
• Skewness and Kurtosis − Skewness refers to the degree of asymmetry of a distribution, and kurtosis refers to the degree of peakedness of a distribution.
• Bias and Variance − They describe the sources of error in a model’s predictions.
• Hypothesis − It is a proposed explanation or solution for a problem.
• Linear Regression − It is used to predict the value of a variable based on the value of another variable.
• Logistic Regression − It estimates the probability of an event occurring.
• Principal Component Analysis − It is a dimensionality reduction method used to reduce the dimensionality of large datasets.

Types of Statistics

There are two types of statistics – descriptive and inferential statistics. 

Descriptive Statistics

Descriptive statistics is a part of statistics that helps us quickly understand and summarize data. It includes basic measures like the mean (average), median (middle value), mode (most frequent value), variance, and standard deviation (how spread out the data is). These measures help us get a clear idea of the data’s overall behavior, like where most values are centered, how much they vary, and how they are distributed.

Applications in Machine Learning
In machine learning, descriptive statistics can be used to summarize the data, identify outliers, and detect patterns. For example, we can use the mean and standard deviation to describe the distribution of a dataset. In Python, we can calculate descriptive statistics using libraries such as NumPy and Pandas. Below is an example −

This will output a summary of the dataset, including the count, mean, standard deviation, minimum, and maximum values as follows −

Inferential Statistics

Inferential statistics is a part of statistics that helps us make predictions or draw conclusions about a larger group (called a population) by looking at a smaller part of it (called a sample). Instead of studying every single data point, we study a sample and use methods like hypothesis testing, confidence intervals, and regression analysis to make educated guesses about the whole population. This is useful when it’s not possible or practical to collect data from everyone.

Applications in Machine Learning
In machine learning, inferential statistics can be used to make predictions about new data based on existing data. For example, we can use regression analysis to predict the price of a house based on its features, such as the number of bedrooms and bathrooms. In Python, we can perform inferential statistics using libraries such as Scikit-Learn and StatsModels. Below is an example −