17 Oct 2023
In ML the hypothesis is the equation or formula that your model uses to make predictions based on input data.
Imagine you have a dataset of houses with features like size, number of bedrooms, and location, and you want to predict their prices. Your hypothesis function might look like this:
H(size, bedrooms, location) = w_1 * size + w_2 * bedrooms + w_3 * location + b
Where
H represents your hypothesis function.size, bedrooms, and location are the input features of a house.w_1, w_2, w_3 are the weights (parameters) that your model learns during training to adjust the importance of each feature.b is the bias term, which accounts for factors not directly related to the input features.During the training process, your machine learning algorithm tries to find the best values for w1 , w2, w_3 and b that minimize the difference between the predicted prices and the actual prices in your training dataset. Once trained, this hypothesis function can be used to predict the prices of new houses based on their size, bedrooms, and location.