Parameters

Let us dive deep into these parameters and understand them in detail:

• max_iter (int, default=100): The first parameter that we will understand is the max_iter parameter. Its main task is to specify the maximum number of iterations also known as epochs which the perceptron will go through during training. If we increase the value then the algorithm will train longer. There is a chance that this can improve the performance.
• tol (float, default=1e-3): The tol stands for tolerance. This tolerance can be thought of as a level required for the stopping criterion for training. It is generally helpful for preventing the overfitting.
• eta0 (float, default=1.0): The eta0 parameter acts as the learning rate. This means that it has ability to determine the size of weight updates during every iteration.
• fit_intercept (bool, default=True): When the fit_intercept parameter is set True which is the default value, the model includes the bias term in the decision function.
• shuffle (bool, default=True): This one is self understanding by its name. When the shuffle parameter is True then the training data is shuffled at the beginning.
• random_state (int or RandomState instance, default=None): It is used for seed initialization. It is eligible to ensure that there random weight initialization and data shuffling.
• verbose (int, default=0): The verbose parameter is mainly used to control the verbosity level for progress messages during training. If there is a value of 0 then it denotes that no progress messages are printed.
• warm_start (bool, default=False): When the warm_start parameter is set to True, it allows us to reuse the solution from a previous call.

Machine learning is a prominent technology in this modern world and as years go by it is growing immensely. There are several components involved in Machine Learning that make it evolve and solve various problems and one such crucial component that exists is the Perceptron. In this article, we will be learning about what a perceptron is, the history of perceptron, and how one can use the same with the help of the Scikit-Learn, library which is arguably one of the most popular machine learning libraries in Python.

Frank Rosenblatt led the development of perceptron in the late 1950s. It is said that this was one of the earliest supervised learning algorithms that did exist. The primary reason behind developing a perceptron was to classify the given data into two categories. So we are confident enough to claim that a perceptron is a type of artificial neural network, that is actually based on real-life biological neurons which in turn makes it a binary classifier.