Multiple machine learning algorithms could be used in the same model to improve the prediction obtained by any of the algorithms used on its own. This is also known as an ensemble method. There are many resources on the internet explaining what ensemble methods are so it will not be repeated here. A Youtube video […]
A machine learning pipeline is the overall process from data processing up till model prediction of the data. It could be considered as being a sequence of actions (e.g. data imputation / data scaling / encoding of categorical values / hyperparameter tuning / model selection) which could be broken down into smaller ‘components’ or functions. […]
In machine learning, a suitable model has to be first chosen for the fitting of a particular dataset. However, the choice of a suitable model is not the end of the story. Each machine learning model has what is known as hyperparameters, which is a parameter whose value is used to control the learning process […]
This post will be looking at using the k-Nearest Neighbour (kNN) algorithm for a classification problem. Suppose there is a new data point that is required to be classified into one of the known categories that comprises the data. This algorithm will look at the k closest points (where k is an integer specified by […]
Evenings With Python 