# Top 8 reasons why one should learn statistics for machine learning

Statistics is one of the mathematical branches which is universally agreed that the knowledge of statistics is mandatory for machine learning. Statistics has a wide range of study which involves findings and theories, notations, and the nuts and bolts tools which are taken into consideration for the machine learning practitioners. Therefore, it becomes necessary to have a solid understanding of statistics for machine learning. This blog will provide you the basic concept for machine learning. Let’s check the details on it.

## What are statistics and machine learning?

Machine learning is one of the subfields of artificial intelligence and computer science, which deals with the system building which is learned from the given data, rather than explicitly coded instructions.

Whereas, statistics is the subfield of mathematics, which is used to calculate the largely ranged data and then represent it in an understandable manner.

Statistics and machine learning are two interlinked fields of the study. Therefore, the statisticians usually refer the machine learning as “statistical learning” or “applied statistics” instead of the centric name of the computer science subject. Machine learning might be a universal representation for the beginners predicting that the students have some knowledge of statistics.

### Reasons why learner should have the knowledge of statistics for machine learning

There are several reasons why it is necessary to learn statistics for machine learning that are described below:

### Preparing the data

The statistics concepts are needed for data preparation of tests that can be utilized in machine learning models.

The statistics for machine learning use several methods for

• Missing value imputations.
• Data scaling.
• Outlier detections.
• Data samplings.
• Variable encodings.

The basic knowledge of descriptive statistics, data distributions, and data visualization are needed, which can help you to identifies the techniques for performing these tasks.

### Evaluating the Models

Statistics techniques are required to evaluate the skills of machine learning samples on the data, which is not visualized during training.

These techniques are used for:

• Data resampling.
• Data sampling.
• Experimental designs.

The machine learning practitioners easily learn resampling methods like k-fold cross-validation.

### Model Selection

Statistics techniques are used to select the model configuration or final models that are used to predict the modeling problems.

### Statistics techniques are used to

• Quantify the size of the results differences.
• Check the significant result differences.

It is used to evaluate the statistical hypothesis tests.

### Model Presentations

Statistics knowledge is needed for presenting the abilities of final samples to stakeholders. The model presentation involves the methods for:

• Quantify the expected variability of the model in practice.
• Conclude the expected skills of the samples on average.
• Estimate the statistics values like confidence intervals.

### Predictions

When there is a need for predicting the values of final models, statistical techniques are used for new data predictions. These methods are consists of:

• Quantify the expected predictive values for the variability of the data.
• Estimate the statistics data for prediction intervals.
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### Problem Framing

Problem framing is one of the biggest leverage that required the selection of the kind of problem, like classifications or regression, and the input and structure of the output and input of the problems. For the beginners to a domain system, it is essential to have a significant exploration of the domain’s observations.

For domain, professionals use statistical techniques that help to explore the data while framing the problems, which involve:

• Data mining: It is used to pattern the data and automatic discoveries of structural relationships.
• Exploratory data analysis: Visualization and conclusions are required to explore the basic idealogy of the data.

### Data Cleaning

Nowadays, most of the data is available in the digital form; when this data has processed, there is a possibility of damaging the fidelity of the information is there. This can lead to downstream the models or processes; that is why there is a need for data cleaning that includes:

• Data error corrections.
• Corrections of data corruptions.
• Data loss corrections.

The process of repairing issues and identifying the data is called data cleaning. The examples of statistical techniques are used to clean the data:

• Imputation: The techniques for filling in missing or corrupt values or repairing the values of the observations.
• Outlier detection: Techniques for identifying the observations which are not nearer to the expected value in a given distribution.

### Model Configuration

The algorithms of machine learning have a suite for hyperparameters, which allows the learning techniques to change the particular problem. The hyperparameters’ configurations are empirical in mature, instead of analyzing the data, they need a large suite of models in order to analyze the effects of hyperparameter values for the models.