Naïve Bayes is a simple but powerful classifier based on a probabilistic model derived from the Bayes' theorem. Basically it determines the probability that an instance belongs to a class…
Decision trees are very simple yet powerful supervised learning methods, which constructs a decision tree model, which will be used to make predictions. The main advantage of this model is…
continued from part 1 In [8]: print_faces(faces.images, faces.target, 400) Training a Support Vector Machine Support Vector Classifier (SVC) will be used for classification The SVC implementation has different important parameters; probably…
Support Vector Machines has become one of the state-of-the-art machine learning models for many tasks with excellent results in many practical applications. One of the greatest advantages of Support Vector…
We will compare several regression methods by using the same dataset. We will try to predict the price of a house as a function of its attributes. In [6]: import numpy…
k-Nearest Neighbors(kNN) Pros: High accuracy, insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values We have an existing…
This blog is from the book and aimed to be as a learning material for myself only.Linear Classification method implements regularized linear models with stochastic gradient descent (SGD) learning. Each sample estimates…
This part is a tutorial using Kobe Bryant Dataset - Part 2. The dataset is from Kaggle and the comprehensive post is divided into multiple parts. This is continued from…