Supervised Machine Learning – A Quick Introduction With Examples [Latest]

As we all know, Machine Learning is a domain which makes use of various algorithms to perform predictions on various sets of data.

These algorithms yield predictions by learning from the data and then perform predictions on the test data to show relevant patterns.

There are basically three types of Machine Learning Algorithms:

• Supervised ML
• Unsupervised ML
• Reinforcement Learning

Today, we will be focusing on Supervised Machine Learning and its various models in detail.

So, without any further delay, let us begin!

First, what’s Supervised Machine Learning?

Supervised Machine Learning is a branch of Machine Learning wherein the model learns from the input provided to it handy before the prediction.

That is, we provide historic data or in simple language, we provide the algorithms with the datasets collected through various means such as surveys, analysis, scarping, etc. Further, we feed the data to the model and it learns from the data to analyse and detect patterns into the data.

At last, we analyze the efficiency of the model by feeding the model to the test dataset and make predictions out of it. This makes us understand to what extent the model satisfies the data in terms of efficiency.

There are more than 100 Supervised Machine Learning algorithms. Let us have a look at some of the most frequently used ML algorithms in detail.

Here we go!!!!! 🙂

1. Decision Trees

Decision Tree is a Supervised Machine Learning model that delivers the predictions on the data values in terms of the chances or probabilities based on rules. It develops rules or event of chances of occurrence based on the training data from which it learns the actions.

In a simple language, we can think of a Decision Tree as a flowchart that works on the rules created from the training set based on the chances of the occurrence of events as in if else rules.

Please visit the below link to find a complete package of explanation and implementation of decision trees – Decision Trees – A Practical Approach

2. Linear Regression

Linear Regression is a Regression Machine Learning model i.e. it works only for numeric variables. In linear regression, we try to predict the value of the response or target variables from the independent data variables. Thus, the model tries to learn how well the independent variables give accurate information about the response value.

The practical implementation of Linear regression will be out of scope for the topic at hand. But we have you covered. Visit the link below and implement your first Linear regression model in Python – Linear Regression – Practical Implementation

3. Logistic Regression

In logistic regression, the model tries to predict the outcome of the binary dependent variable such as true/false, yes/no, male/female, etc using a logit function.

This logit function depends upon the probability and the odds value to predict the value of the binary response variable out of chances or probabilities.

While we are yet to cover a practical implementation of Logistic regression on our website, have a look at the sklearn library’s Logistic regression model module.

Advantages of Supervised Machine Learning models

• Supervised Machine Learning Algorithms help us predict the values of the test dataset based on the prior evaluations.
• It enables us have a real life solution for problems such as email spam filtering, loan defaulter detection ,etc.

Limitations faced in Supervised Machine Learning models

• Very less information is available in terms of the class of the object.
• The complexity of the model rises exponentially with increase in the complexity of the data.

Conclusion

By this, we have come to the end of this topic. Feel free to comment below, in case you come across any question. For more such posts related to Machine Learning in Python, Stay tuned @ Machine Learning with JournalDev, and till then Happy Learning with JournalDev!! 🙂