Supervised learning is related to creating a model which can be used for forecasting based on the historical data for unseen data. The machine learning technique reads the input data set and the expected output data. The model is trained for forecasting the outputs for the new scenarios.
The supervised machine learning can be categorized as:
The fitting of the data is done in the Regression method. The data is partitioned in the Classification method. Supervised learning is very popular in the machine learning space.
The input variables z is transformed by the mapping function g to create the output variable W in supervised learning technique.
W = g(Z)
The new input data Z will be used for forecasting the output variables W using the mapping function. The aim is to find the mapping function. This method is referred to as supervised learning as it is like a manager supervising the employee learning process. Supervisor checks the training process and the forecasts on the training data set. The supervisor validates the outputs for unseen data and the technique targets a goal set for effectiveness.
Let us look at the examples for classification in the following section. The first example is related to classification of dogs.
Classification of Dogs
There are different types of dogs. Dogs can be classified into the following groups.
Dogs have different characteristics and each group has set of features which are used to identify the dog. This is a good example for supervised learning where we have to classify the dog images into various groups based on features.
There are around 560 breeds of dogs presented in the word cloud below:
Another example is classification of cats.
Below is the word cloud of 100 cat breeds. Each breed has different characteristic and feature to categorize the images.
Some of the features or characteristics of the cat are body type, coat, pattern of the skin and coat. The shape of the face is another important factor for cat classification.
Note :A chowder is a set of cats. It is also referred as a glaring. The cats which are very different to each other in a group, glaring is the right word. Kindle is a group of kittens.
In the case of regression, data is distributed in different dimensions. Information needs to be retrieved from it.The models need to evolved based on the data set and the errors need to be minimized for prediction. Regression is the method which is described above.
Dogs and cats problems have different challenges and learning is different in each case. Features need to be analyzed and the models need to be fitted to the data available for prediction.
In terms of machine learning we define these two types as a part of broader class called supervised learning. Machine learning has evolved with the data and processing power available at that particular time.