Descriptive statistics is a branch of statistics that deals with the collection, analysis, and presentation of data. It is concerned with summarizing and describing the features of a dataset. Descriptive statistics provides a way to organize and summarize data in a meaningful way.
Here are some examples of descriptive statistics:
Measures of central tendency: These measures describe the central or typical values in a dataset. The three most common measures of central tendency are mean, median, and mode. For example, if you have a
dataset of test scores (80, 85, 90, 95, 100), the mean score is (80+85+90+95+100)/5= 90, the median score is 90,
and the mode score is not applicable since there are no repeating scores.
Measures of dispersion: These measures describe how spread out the data is. The most commonly used measure of dispersion is the standard deviation. For example, if you have a dataset of monthly salaries for a
group of employees (2500, 3000, 3500, 4000, 4500), the standard deviation is approximately 869.64.
Frequency distribution: This describes how often each value occurs in a dataset. For example, if you have a dataset of the number of hours students spend on
homework per week (5, 10, 10, 15, 15, 15, 20, 20, 25),
the frequency distribution shows that there are two students who spend 5 hours, two students who spend 10 hours, three students who spend 15 hours, two students who spend 20 hours, and one student who spends 25 hours on homework per week.
Measures of skewness and kurtosis: These measures describe the shape of the distribution of the data. Skewness measures the degree of asymmetry in the distribution, while kurtosis measures the degree of peakedness in the distribution. For example, if you have a dataset of heights of a sample of individuals and the distribution is skewed to the left, the skewness measure will be negative, and if the distribution is highly peaked, the kurtosis measure will be high.
Descriptive statistics helps to summarize data in a meaningful way and provides insights into various aspects of the data.
Descriptive statistics plays an important role in machine learning by providing insights into the properties of data, which is a critical step in building and evaluating machine learning models. Here are some examples:
Data preprocessing: Before applying machine learning algorithms, data must be preprocessed, which often involves performing descriptive statistics. For example, data normalization or standardization is a common preprocessing step that uses descriptive statistics such as mean and standard deviation to transform the data to have a standard scale.
Feature selection: Descriptive statistics can help in identifying the most relevant features to use in a machine learning model. For example, a correlation matrix can be used to identify which features are strongly correlated with the target variable, and can thus be included in the model.
Model evaluation: Descriptive statistics can be used to evaluate the performance of machine learning models. For example, measures of accuracy, precision, recall, and F1-score are commonly used to evaluate classification models.
Outlier detection: Descriptive statistics can help to identify outliers in the data, which can have a significant impact on machine learning models. For example, a box plot can be used to identify outliers in a dataset, which can then be removed or treated appropriately.
In summary, descriptive statistics plays an important role in various aspects of machine learning, including data preprocessing, feature selection, model evaluation, and outlier detection. It helps to provide insights into the properties of the data, which can improve the performance and reliability of machine learning models.