Distribution Analysis in Machine Learning
Distribution analysis is a vital aspect of machine learning that involves understanding how the variables (or features) in a dataset are spread and how they relate to each other. This kind of analysis helps in identifying patterns, outliers, skewness, kurtosis, and other important statistical attributes of the data.
A deep understanding of the distributions of your data can greatly improve the quality of your machine learning models by informing the kind of preprocessing and feature engineering steps you need to take. For example, it can help in deciding whether to normalize or standardize the data, or which type of machine learning algorithm might work best.
A sample of data will form a distribution, and by far the most well-known distribution is the Gaussian distribution, often called the Normal distribution. A distribution is simply a collection of data, or scores, on a variable. Usually, these scores are arranged in order from smallest to largest and then they can be presented graphically
This involves cleaning the data, dealing with missing values, and transforming variables to make them suitable for machine learning models. It may also involve one-hot encoding, normalizing, or standardizing the data depending on the distribution of the variables.
Exploratory Data Analysis (EDA)
EDA is a crucial step in understanding the distribution of the data. It involves summarizing main characteristics of the data, often visualizing them in the form of histograms, box plots, scatter plots, etc.
Companies may offer to create new features from existing ones, or transform existing features to better capture the underlying distribution of the data.
This involves applying statistical tests to understand if the distribution of a particular feature is significantly different in different groups of data. Examples of such tests include the t-test, chi-square test, ANOVA, etc.
Model Selection and Tuning
Understanding the distribution of your data can greatly inform the selection and tuning of your machine learning model. Companies may offer services to help select and tune a machine learning model that best fits the distribution of your data.
Interpretation and Reporting
We offer services to help interpret the results of the machine learning model, including understanding how the distribution of the data impacts the model's predictions. This often involves creating clear, visually appealing reports that explain the findings in a way that non-technical stakeholders can understand.
Type of Distribution
In statistics, there are several types of data distributions that are frequently used in the field of data analysis, machine learning, and statistical inference. These distributions help us to understand the underlying patterns and characteristics of the data. Here are some common types:
Normal Distribution (Gaussian distribution): The most common symmetric bell-shaped distribution. It is defined by its mean (µ) and standard deviation (σ). The bulk of the observations lie around the mean.
Uniform Distribution: In this distribution, all outcomes are equally likely. A deck of cards has a uniform distribution because the likelihood of drawing a heart, club, diamond or spade is equally likely.
Binomial Distribution: This distribution is used when there are exactly two mutually exclusive outcomes of a trial (often referred to as a success and a failure). The binomial distribution is used when there are n independent trials.
Poisson Distribution: This distribution applies when events occur randomly and independently over time or space. It's often used to model counts, such as the number of emails arriving in your inbox in a given hour.
Exponential Distribution: This distribution describes the time between events in a Poisson process, i.e., a process in which events occur continuously and independently at a constant average rate.
Chi-Square Distribution: This is a distribution of a sum of the squares of k independent standard normal random variables. It's often used in hypothesis testing and in constructing confidence intervals.
Log-Normal Distribution: A distribution is log-normal if the logarithm of the variable follows a normal distribution. Log-normal distributions can model a wide range of phenomena in natural and social sciences.
Beta Distribution: This is a family of continuous probability distributions defined on the interval [0, 1] parameterized by two positive shape parameters. It is a suitable model for the random behavior of percentages and proportions.
Gamma Distribution: It is a two-parameter family of continuous probability distributions which includes exponential distribution and chi-squared distribution as special cases.
Each distribution has a different shape and is defined by certain parameters. These distributions help data scientists and statisticians draw inferences and make predictions. Different types of data and different domains require different types of distributions for analysis.
Left Skewed Distribution:
When data points cluster on the right side of the distribution, then the tail would be longer on the left side. This is the property of Left Skewed Distribution. The tail is longer in the negative direction so we also call it Negatively Skewed Distribution
Right Skewed Distribution:
When data points cluster on the left side of the distribution, then the tail would be longer on the right side. This is the property of Right Skewed Distribution. Here, the tail is longer in the positive direction so we also call it Positively Skewed Distribution.
Visualization Techniques use in distribution
A Histogram visualizes the distribution of data over a continuous interval
Each bar in a histogram represents the tabulated frequency at each interval/bin
In simple words, height represents the frequency for the respective bin (interval)
Histogram results can vary wildly if you set different numbers of bins or simply change the start and end values of a bin. To overcome this, we can make use of the density function.
A density plot is a smoothed, continuous version of a histogram estimated from the data. The most common form of estimation is known as kernel density estimation (KDE). In this method, a continuous curve (the kernel) is drawn at every individual data point and all of these curves are then added together to make a single smooth density estimation.