Random forests use an ensemble learning method for classification or regression. A random forest classifier is used to solve classification problems. When we train a random forest with training data, it generates several decision trees. And then, when input features are provided, the random forest selects the class that is selected by most of the trees in the random forest. In our previous articles, we discussed classification trees and regression trees.

In the repeated random train-test split or shuffle split, the dataset is split into a certain number of folds. Each fold is divided into train and test sets. The machine learning model then uses the train test to learn from the dataset and uses the test set to evaluate the model. We can use the following Python code to implement the repeated random train-test split or shuffle split. Here, we are first using the pandas Python library to read the Pima Indians Diabetes dataset.

In one of our previous articles, we discussed how to calculate the test statistic in a chi-square test of independence or goodness-of-fit test. We also discussed that the test statistic in a chi-square test follows the chi-square distribution. So, how can we calculate the p-value from the test statistic in a chi-square test? In this article, we will discuss that. Let's say our test statistic is 6.4, and the degrees of freedom is 5. Here, we are using the chi2.sf()

The test statistic in the chi-square goodness-of-fit test or the test of independence follows the chi-square distribution. The chi-square distribution graph is asymmetrical in shape. It is skewed to the right, and its shape depends on the degrees of freedom. We can use the following Python code to generate the chi-square distribution graph for specific degrees of freedom. Here, we are using the linspace() function from the numpy Python module to generate 300 equally spaced numbers within the range 0 to 30.

We can calculate the p-value from the test statistic in an F-test using the scipy.stats.f Let's say we are performing a one-way ANOVA test. The test statistic, in this case, follows the F-distribution. So, ANOVA is also an F-test. Let's say there are three distinct groups of students.

We can use the following Python code to calculate the critical value for a left-tailed, right-tailed or two-tailed t-test for a specific degrees of freedom and significance level. Here, we are using the t.ppf() function from the scipy.stats The t.ppf() function is a percent point function.

We can plot Gaussian distribution easily using Python. In this article, we will discuss how to plot normal distribution using matplotlib module in Python. To plot the normal distribution, we will first generate evenly spaced numbers within a specific range. The following piece of Python code will generate evenly spaced 100 numbers within the range [-3, 3]. Please note that the generated numbers may include the endpoint (here 3) depending on whether the endpoint parameter of the linspace() function is True or not.

In Python, we can use the randn() function to generate random numbers from the normal distribution. Here, the randn() function will return 10000 floating point random numbers from the standard normal distribution. Therefore, the mean of the generated numbers will be 0, and the standard deviation will be 1. After generating 10000 such random numbers from the standard normal distribution, we can plot the distribution using a histogram.

A categorical variable contains categorical data, such as name, gender, address, etc. There are different types of categorical variables. A nominal categorical variable contains categorical data that cannot be ranked over each other. For example, name, address, gender, etc. But let's say there is a categorical variable that contains categorical data that can be ranked over each other.

Machine learning algorithms understand only numbers. So, if a column in a dataset contains categorical values, we need to encode the categorical values into numbers. We can use label encoding for that purpose. In label encoding, labels are replaced by integers. So, it is also called integer encoding.