Training Set

What is a Training Set :

A training set is a collection of data used to teach a machine learning model to recognize patterns and make predictions. It is a crucial part of the machine learning process, as the model will use the training set to learn how to classify or predict outcomes for new data.
Example 1:
Imagine you are building a machine learning model to classify different types of fruit based on their size, shape, and color. You have a training set of 1000 different fruits, including apples, oranges, bananas, and pears. You use this training set to teach the model how to classify each fruit based on its characteristics.
For example, the model might learn that apples are typically round, red or green, and medium in size, while bananas are long, yellow, and medium in size. The model will use this information to make predictions about new fruits it has not seen before. If the model is presented with a fruit that is round, red, and medium in size, it will likely predict that it is an apple.
Example 2:
Another example of a training set could be a collection of customer data used to predict whether or not a customer will churn (cancel their subscription or service). The training set might include information about each customer’s age, gender, income level, and how long they have been a customer.
Using this training set, the model could learn that customers who are younger and have a lower income level are more likely to churn, while customers who are older and have a higher income level are less likely to churn. The model could then use this information to predict whether a new customer is likely to churn or not.
It is important to note that the size and quality of the training set can greatly impact the performance of the machine learning model. A larger, more diverse training set will typically result in a more accurate model, while a smaller or less diverse training set may lead to a less accurate model. It is also important to carefully curate the training set to ensure it is representative of the data the model will encounter in the real world.