Supervised Vs Unsupervised Learning
Guys! If you’re beginners, No worries too much with detail…. Let’s have simple then you can build your confidence. Here I am narrating the high level difference and understanding and steps to implement the same.
Supervised learning is simply a process of learning algorithm from the training dataset.
Unsupervised learning is modeling the hidden structure in the data in order to learn and understand given data. On top only we have input data and no corresponding output variables as in Supervised learning model.
Simply remember the following table….
Supervised Machine Learning
o In supervised learning, we use to train our model using labelled dataset.
o For Test and Train perspective, we should split our dataset into a training dataset and test dataset (75%- 25%)
o Training dataset is used to train the model
o Test dataset acts as new data for predicting results and compares the accuracy of our model against Training dataset results.
o Generally, the Supervised models are performing fast that other ML, because the training time taken is less as we already have desired results in our dataset.
o Supervised model predicts accurate results on unknown/unseen dataset or new dataset
Supervised Machine Learning –Linear Regression ( Simple steps)
Go ahead! and write your simple Linear Regression model using above steps, Surely you can write a very quick LR model with good understanding.
Will get back to you with few other concepts in easy and simple steps!
Cheers! Shantha
