Supervised Learning is the process of making an algorithm to learn to map an input to a particular output. This is achieved using the labelled datasets that you have collected. Supervised Learning algorithms can help make predictions for new unseen data that we obtain later in the future.
Supervised learning provided with two sets of data(if dataset is smaller), a training set and a validation set and test set. but provide three sets of data(if dataset is greater) , a training set , a validation set and test set.
The idea is for the model to “learn” from a set of labeled examples in the training set so that it can identify unlabeled examples in the test set with the highest possible accuracy.
There are many different approaches that attempt to build the best possible method of classifying examples of the test set by using the data given in the training set.
In supervised learning, the training set consists of n ordered pairs (x1, y1), (x2, y2), ...,(xn, yn), where each x1 is some parameters of data, and y1 is the label for that data .
For example, an x might be a group of five parameters for a patient in a hospital including height, weight, temperature, blood sugar level, and blood pressure. The corresponding yi might be a classification of the patient as “healthy” or “not healthy”.
Supervised learning can be done in two ways:
Regression analysis helps us to understand how the value of the dependent variable is changing corresponding to an independent variable when other independent variables are held fixed. It predicts continuous/real values such as temperature, age, salary, price, etc.
It is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between a series of other variables and one dependent variable.
Linear Regression
Logistic Regression
Support Vector Regression
Decision Tree Regression
Classification is a process of categorizing a given set of data into classes.The process starts with predicting the class of given data points. The classes are often referred to as target, label or categories.
Classification is a type of supervised learning. It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well.
Random Forest classifier
Decision Trees classifier
support vector machine classifier
Nearest Neighbour classifier
- Supervised learning is a simple process for to understand.
- Easily identifies trends and patterns
- Supervised machine learning is to predict a target numerical value from some given data and labels.
- No human intervention needed (automation)
Machine Learning is a technique of training machines to perform the activities a human brain can do, albeit bit faster and better than an average human-being. Today we have seen that the machines can beat human champions in games such as Chess, AlphaGO, which are considered very complex.
Machine Learning can be a Supervised or Unsupervised. If you have lesser amount of data and clearly labelled data for training, opt for Supervised Learning.
Machine Learning is a technique of training machines to perform the activities a human brain can do, albeit bit faster and better than an average human-being. Today we have seen that the machines can beat human champions in games such as Chess, AlphaGO, which are considered very complex.