Supervised and unsupervised learning in machine learning
Supervised learning:-
Supervised learning algorithms are trained using labeled data. The Supervised learning model takes direct feedback to check if it is predicting correct output or not.
Supervised learning model predicts the output. In supervised learning input data is provided to the model along with the output. The goal of supervised learning is to train the model so that it can predict the output when it is given new data.
Supervised learning needs supervision to train the model. Supervised learning can be categorized in classification and regression problems. Supervised learning can be used for those cases where we know the input as well as corresponding outputs.
Supervised learning model produces an accurate results. Supervised learning is not close to true artificial intelligence as in this, we first train the model for each data & then only it can predict the correct output. It includes various algorithms such as linear regression, logistic regression, super vector mach, multi class classification, decision tree, Bayesian logic etc.
Example: Suppose we have an image of different types of fruits. The task of our supervised learning model is to identify the fruits and classify them accordingly. So to identify the image in supervised learning, we will give the input data as well as output for that, which means we will train the model by the shape, size, color, and taste of each fruit. Once the training is completed, we will test the model by giving the new set of fruit. The model will identify the fruit and predict the output using a suitable algorithm.
Unsupervised learning:-
Unsupervised learning algorithms are trained using unlabeled data. Unsupervised learning does not take any feedback. Unsupervised learning model finds the hidden patterns in data. In unsupervised learning, only input data is provided to the model.
The goal of unsupervised learning is to find the hidden patterns & useful insight from the unknown dataset. Unsupervised learning not need any supervision to train the model. Unsupervised learning can be classified in clustering & associations problem. Unsupervised learning can be used for those cases where we have only input data & no corresponding output data.
Unsupervised learning model may give less accurate result as compared to supervised learning. Unsupervised learning is more close to the true artificial intelligence as it learns similarly as a child learns daily routine things by his experiences. It includes various algorithms such as clustering, KNN & aprior algorithm.
Example: To understand the unsupervised learning, we will use the example given above. So unlike supervised learning, here we will not provide any supervision to the model. We will just provide the input dataset to the model and allow the model to find the patterns from the data. With the help of a suitable algorithm, the model will train itself and divide the fruits into different groups according to the most similar features between them.
Supervised learning algorithms are trained using labeled data. The Supervised learning model takes direct feedback to check if it is predicting correct output or not.
Supervised learning model predicts the output. In supervised learning input data is provided to the model along with the output. The goal of supervised learning is to train the model so that it can predict the output when it is given new data.
Supervised learning needs supervision to train the model. Supervised learning can be categorized in classification and regression problems. Supervised learning can be used for those cases where we know the input as well as corresponding outputs.
Supervised learning model produces an accurate results. Supervised learning is not close to true artificial intelligence as in this, we first train the model for each data & then only it can predict the correct output. It includes various algorithms such as linear regression, logistic regression, super vector mach, multi class classification, decision tree, Bayesian logic etc.
Example: Suppose we have an image of different types of fruits. The task of our supervised learning model is to identify the fruits and classify them accordingly. So to identify the image in supervised learning, we will give the input data as well as output for that, which means we will train the model by the shape, size, color, and taste of each fruit. Once the training is completed, we will test the model by giving the new set of fruit. The model will identify the fruit and predict the output using a suitable algorithm.
Unsupervised learning:-
Unsupervised learning algorithms are trained using unlabeled data. Unsupervised learning does not take any feedback. Unsupervised learning model finds the hidden patterns in data. In unsupervised learning, only input data is provided to the model.
The goal of unsupervised learning is to find the hidden patterns & useful insight from the unknown dataset. Unsupervised learning not need any supervision to train the model. Unsupervised learning can be classified in clustering & associations problem. Unsupervised learning can be used for those cases where we have only input data & no corresponding output data.
Unsupervised learning model may give less accurate result as compared to supervised learning. Unsupervised learning is more close to the true artificial intelligence as it learns similarly as a child learns daily routine things by his experiences. It includes various algorithms such as clustering, KNN & aprior algorithm.
Example: To understand the unsupervised learning, we will use the example given above. So unlike supervised learning, here we will not provide any supervision to the model. We will just provide the input dataset to the model and allow the model to find the patterns from the data. With the help of a suitable algorithm, the model will train itself and divide the fruits into different groups according to the most similar features between them.
Comments
Post a Comment