Machine Learning is a set of algorithms that train on a data set to make
predictions or take action in order to optimize some system.
Like as supervised classification algorithms are used to classify
potential clients into good or bad prospects, for lone purposes based on the
historical data.
A Every beginner come in mind one question, Machine learning is very
wide,” Which algorithm should I use? “The answer to the question varies
depending on many factors
- The size, quality, and nature of data.
- The available computational time.
- The urgency of the task.
- What you want to do with the data.
Even an experienced data scientist cannot tell which algorithm will
perform the best before trying different algorithms.
Supervised Learning:
Supervised learning algorithms make prediction based on a set example.
For example, historical sales can be used to estimate the future prices. With
supervised learning, you have an input variable that consists of labelled
training data and desired output variable.
You use an algorithm to analyse the training data to learn the function
that maps the input and output.
- Classification: When the data are being used to predict a categorical variable, supervised learning is also called classification. This is the case when assigning a label or indicator, either dog or cat to an image.When there are only two labels, this is called binary classification. when there are more then two categories, the problems are called multi-call classification.
· Regression:When predicting continuous
values, the problem become a regression problem.
- Forecasting:This is the process of making predictions about the future base on the past and present data. It is most commonly used to analyse trends. A common example might be estimation of the next year sales based on the sales of the current year and previous years.