Analyze univariate, bivariate, and multicollinearity problems in machine learning

Univariate

Univariate data analysis is a simple type of analysis suitable for only one variable that changes. It mainly focuses on the description and pattern recognition of data, but not on causes and relationships. Because information deals with a single variable, it is the simplest type of analysis.

Univariate analysis is used to analyze a single variable/feature. The goal is to take the data and describe and summarize it while examining any patterns that may exist. Univariate analysis studies each variable in the data set separately and can use both categorical and numerical variables.

Measures of central tendency (mean, median, and mode) and data dispersion or distribution (range, minimum, maximum, quartiles, variance, and standard deviation ) can help us describe patterns in such data. Additionally, tools such as frequency distribution tables, histograms, pie charts, frequency polygons, and bar charts can be used to demonstrate these patterns.

Dual variable

Bivariate data involves two variables. Bivariate analysis focuses on causes and relationships, with the goal of determining the relationship between two variables.

Comparisons, correlations, causes, and explanations are all part of bivariate data analysis. One of the variables is independent while the other is dependent, and these variables are often plotted on the X and Y axes of the chart for a better understanding of the data.

Multicollinearity

Multicollinearity (also known as collinearity) is a statistical phenomenon in which a characteristic in a regression model A variable has a high linear correlation with another feature variable. When two or more variables are perfectly correlated, this is called collinearity.

When the independent variables are highly correlated, changes in one variable will cause changes in other variables, causing the model results to fluctuate greatly. If the data or model changes slightly, the model results will be unstable and fluctuate widely. Multicollinearity can lead to the following problems:

If the model provides different results every time, it becomes difficult to determine the list of important variables for the model.

The coefficient estimates will be unstable, making it difficult to interpret the model. In other words, if a predictor changes by one unit, there is no way to determine how much the output will change.

Due to the instability of the model, overfitting may occur. When the model is applied to another set of data, the accuracy will be much lower than the training data set.

If only slight or moderate collinearity occurs, this may not be a problem for the model, depending on the circumstances. However, if there is a serious collinearity problem, it is recommended to solve the problem.

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