In this blog, I will be discussing Supervised Learning and how it uses the Bayesian Model. Machine learning is one of the most, if not the most complex, a subset of the programming discipline. Thankfully, that has not deterred its exploration. One of the techniques used for this is known as Supervised Learning. What is
ML is a type of data analysis that uses algorithms and methods to detect the patterns of new and existing datasets. It is one of the fastest and advanced technologies in the current industry. ML has lots of applications, and data entry automation is one of the important applications. In today’s businesses, data inaccuracy and
Scikit-learn refers to machine learning algorithms as estimators. There are three different types of estimators: classifiers, regressors, and transformers. The inheritance of the second class determines what kind of estimator the model represents. We’ll divide the estimators into two groups based on their interface. These two groups are predictors and transformers, and in this blog,
Ensemble models Ensemble models are machine learning models that use more than one predictor to predict. A group of predictors forms an ensemble. In general, ensemble models perform better than using a single predictor. There are three ensemble models, bagging, boosting, and blending. Random forests The performance of a single decision tree will be limited,
