In supervised learning, overfitting is a major problem. When it comes to regression trees, it becomes more obvious to prevent this, we specify the Seth of the tree, but there is still a high chance of the dataset being overfitted. To avoid this, we use a random first regressor. Before understanding how and why this…
Under supervised learning, there is a type called classification. These algorithms recognize the category a new observation belongs to based on the training dataset. In supervised learning, there are independent variables and a dependent variable Here, the dependent variable is the category, and each category’s features are independent variables. These categories are distinct and pre-defined…
Have you ever experienced the frustration of creating a machine learning model that performs perfectly on your training data, but fails miserably when presented with new data? This is a common problem in the world of AI and it’s known as overfitting. On the other hand, have you ever wondered why your algorithm simply can’t…
Do you ever wonder how machine learning algorithms are trained to make accurate predictions and decisions? Well, it all starts with a crucial component called the dataset. In simple terms, a dataset is a collection of data that is used to teach machine learning models how to recognize patterns and make informed choices. But what…
