25 Important Questions And Answers About Supervised Learning

What is supervised learning in simple words?

Supervised learning, also referred to as supervised machine learning is a subset of artificial intelligence and machine learning. In this method, a computer algorithm is trained on input data that has been labeled for a particular output.

What are the examples of supervised learning?

Some examples of supervised learning include: 1. The user receives a set of pictures with information about what’s on them and then you train a machine to identify new photos. 2.

There are a lot of molecules and details about what are considered drugs. You build a model that can determine whether a new molecule is a drug or not.

Where is supervised machine learning used?

Some of the other practical applications of supervised learning algorithms in real life, including Text categorization, Face Detection, Signature recognition, etc.

What are the different types of supervised learning?

There are two types of Supervised Learning techniques: Regression and Classification. Classification separates the data, Regression fits the data.

What is a regression in machine learning for example?

Regression is a technique for investigating the relationship between independent variables or features and a dependent variable or outcome.

It’s used as a method for predictive modeling in machine learning, in which an algorithm is used to predict continuous outcomes.

Predicting prices of a house given the features of the house like size, price, etc is one of the common examples of Regression.

What is classification in machine learning for example?

Classification refers to a predictive modeling problem where a class label is predicted for a given example of input data.

Some of the examples of classification are 1. Given an example, classify if it is spam or not. 2. Given a handwritten character, classify it as one of the known characters.

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What are the steps of supervised learning?

The steps for supervised learning are:
1. Prepare Data.
2. Choose an Algorithm.
3. Fit a Model.
4. Choose a Validation Method.
5. Examine Fit and Update Until Satisfied.
6. Use the Fitted Model for Predictions.

What is the purpose of supervised learning?

The goal of supervised learning is to build an artificial system that can learn the mapping between the input and the output and can predict the output of the system given new inputs.

How supervised learning work?

In supervised learning, models are trained using a labeled dataset, where the model learns about each type of data.

Once the training process is completed, the model is tested based on test data (a subset of the training set), and then it predicts the output.

How supervised learning works?

What is the difference between classification and regression?

The main difference between Regression and Classification algorithms is that Regression algorithms are used to predict continuous values such as price, salary, age, etc.

Classification algorithms are used to predict/Classify discrete values such as Male or Female, True or False, Spam or Not Spam, etc.

What is the difference between supervised and unsupervised learning?

The main difference between supervised learning and unsupervised learning is that supervised machine learning uses labeled input and output data, while unsupervised machine learning does not.

In supervised learning, the algorithm “learns” from the training dataset by iteratively making predictions on the data and adjusting for the correct answer.

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What is the difference between supervised learning and reinforcement learning?

In supervised learning, the training data has the answer key with it, so the model is trained with the correct answer itself.

In reinforcement learning, there is no answer but the reinforcement agent decides what to do to perform the given task.

What is the difference between reinforcement learning and semi-supervised learning?

Semi-supervised learning takes a middle ground. It utilizes a small amount of labeled data supporting a larger set of unlabeled data.

Reinforcement learning trains an algorithm with a reward system, providing feedback when an artificial intelligence agent performs the best action in a particular situation.

What are the differences between supervised learning unsupervised learning and reinforcement learning?

Supervised learning maps labeled data to known output. Whereas, Unsupervised Learning explores patterns and predicts the output.

Reinforcement Learning follows a trial-and-error method. Shortly in SM, the goal is to generate a formula based on input and output values.

Is random forest supervised or unsupervised?

Random forest is a supervised learning algorithm. The “forest” it builds is an ensemble of decision trees, usually trained with the “bagging” method.

Is linear regression supervised or unsupervised?

In the most simple words, Linear Regression is the supervised Machine Learning model in which the model finds the best fit linear line between the independent and dependent variable i.e. it finds the linear relationship between the dependent and independent variable.

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Is k nearest neighbor unsupervised?

It’s supervised, though some applications use it as part of unsupervised algorithms. For regression or classification, it’s supervised and lazy.

Is CNN supervised or unsupervised?

CNN is neither supervised nor unsupervised. It’s just a neural network that, for example, can extract features from images by dividing them, pooling, and stacking small areas of the image.

If you want to classify images, you need to add dense (or fully connected) layers, and for classification, the training is supervised.

But, if you want to cluster images based on similarities of a group of images, you will extract the features, use the CNN, and then use an unsupervised method like k-means.

Is naive Bayes supervised or unsupervised?

Naive Bayes methods are a group of supervised learning algorithms based on applying the Bayes theorem with the naive assumption of conditional independence between every pair of features given the value of the class variable.

Is k means supervised or unsupervised?

K-means clustering is an unsupervised learning algorithm. There is no labeled data for this clustering, unlike in supervised learning.

K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.

Is logistic regression supervised or unsupervised?

Logistic regression is an example of supervised learning. It is used to predict the probability of a target variable. The nature of the target or dependent variable is dichotomous, which means there would be only two possible classes.

Is image classification supervised or unsupervised?

Image classification is divided into two categories (1) supervised image classification and (2) unsupervised image classification.

In supervised image classification, a training stage is required, which means first we need to select some pixels to form each class called training pixels.

What are the advantages of supervised learning?

Supervised learning has many advantages, such as clarity of data and ease of training, classes represent the features on the ground, and training data is reusable unless features change.

What are the disadvantages of supervised learning?

Some of the disadvantages are
1. Computation time is vast for supervised learning.
2. Unwanted data down efficiency.
3. Pre-processing of data is no less than a big challenge.
4. Always in need of updates.
5. Inability to learn by itself.

Is supervised learning better than unsupervised?

Yes, It is better in many situations as it produces accurate results. An unsupervised learning model may give less accurate results as compared to supervised learning.

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