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1. Top results related to supervised learning algorithms

   • supervised learning,unsupervised learning ,regression

     Top Answer

     Answered Sep 16, 2016 · 16 votes

     1) Linear Regression is Supervised because the data you have include both the input and the output (so to say). So, for instance, if you have a dataset for, say, car sales at a dealership. You have, for each car, the make, model, price, color, discount etc. but you also have the number of sales for each car. If this task was unsupervised, you would have a dataset that included, maybe, just the make, model, price, color etc. (not the actual number of sales) and the best you could do is cluster the data. The example isn't perfect but aims to get across the big picture. A good question to ask yourself when deciding whether a method is supervised or not is to ask "Do I have a way of adjudging the quality of an input?". If you have Linear Regression data, you most certainly can. You just evaluate the value of the function (in this case, the line) for the input data to estimate the output. Not so in the other case.

     2) Logistic Regression isn't actually a regression. The name is misleading and does indeed lead to much confusion. It is usually only used for binary prediction which makes it perfect for classification tasks but nothing else.

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   • Supervised | Learning Quiz | Question 17

     Which algorithm is a type of ensemble learning and combines multiple weak learners to create a strong learner?

     (A)

     Decision Trees

     (B)

     Random Forest

     (C)

     K-Nearest Neighbors

     (D)

     Naive Bayes

     Answer: (B) Explanation:

     Random Forest is an ensemble learning algorithm that combines multiple decision trees to improve predictive accuracy and control overfitting.

     Quiz of this Question Please comment below if you find anything wrong in the above post

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   • Supervised Machine Learning

     A machine is said to be learning from Past Experiences (data feed-in) with respect to some class of tasks if its Performance in a given Task improves with the Experience.

     For example, assume that a machine has to predict whether a customer will buy a specific product let’s say “Antivirus” this year or not. The machine will do it by looking at the previous knowledge/past experiences i.e. the data of products that the customer had bought every year and if he buys an Antivirus every year, then there is a high probability that the customer is going to buy an antivirus this year as well. This is how machine learning works at the basic conceptual level.

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     Table of Content

     • What is Supervised Machine Learning?
     • Types of Supervised Learning Algorithm
     • Regression
     • Classification

     4. Flexibility...

     • 1. Complexity
     • 2. Interpretability
     • 3. Scalability
     • 4. Flexibility

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     3/5

   • Supervised Machine Learning

     A machine is said to be learning from Past Experiences(data feed-in) with respect to some class of tasks if its Performance in a given Task improves with the Experience.

     For example, assume that a machine has to predict whether a customer will buy a specific product let’s say “Antivirus” this year or not. The machine will do it by looking at the previous knowledge/past experiences i.e. the data of products that the customer had bought every year and if he buys an Antivirus every year, then there is a high probability that the customer is going to buy an antivirus this year as well. This is how machine learning works at the basic conceptual level.  

     Check the images on geeksforgeeks

     <img>

     Table of Content

     • What is Supervised Machine Learning?
     • Types of Supervised Learning Algorithm
     • Regression
     • Classification
     • 1. Complexity
     • 2. Interpretability
     • 3. Scalability
     • 4. Flexibility
     • 5. Training Time and Computational Efficiency

     What is Supervised Machine Learning?

     Supervised learning is a machine learning technique that is widely used in...

     Check all answers on Geeksforgeeks

     4/5

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2. www.geeksforgeeks.org › supervised-machine-learningSupervised Machine Learning - GeeksforGeeks

   www.geeksforgeeks.org › supervised-machine-learning

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   Aug 12, 2024 · By reframing it as a supervised learning problem, you can leverage a variety of machine learning algorithms, both linear and nonlinear, to improve the forecasting accuracy. In this article, we will see how we can consider a supervised learning model.

3. en.wikipedia.org › wiki › Supervised_learningSupervised learning - Wikipedia

   en.wikipedia.org › wiki › Supervised_learning

   • Cached

   Supervised learning ( SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as a human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data to expected output values. [ 1]

4. dataaspirant.com › supervised-learning-algorithms10 Most Popular Supervised Learning Algorithms In Machine ...

   dataaspirant.com › supervised-learning-algorithms

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   Mar 17, 2023 · Supervised learning is a type of machine learning where a set of labelled data is used to train a model for future predictions. Labelled data consists of input features and output values, allowing the algorithm to make decisions based on the provided data.

5. www.ibm.com › topics › supervised-learningWhat Is Supervised Learning? | IBM

   www.ibm.com › topics › supervised-learning

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   Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled data sets to train algorithms that to classify data or predict outcomes accurately.

6. scikit-learn.org › stable › supervised_learning1. Supervised learning — scikit-learn 1.5.1 documentation

   scikit-learn.org › stable › supervised_learning

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   Jan 1, 2010 · 1. Supervised learning. 1.1. Linear Models; 1.2. Linear and Quadratic Discriminant Analysis; 1.3. Kernel ridge regression; 1.4. Support Vector Machines; 1.5. Stochastic Gradient Descent; 1.6. Nearest Neighbors; 1.7. Gaussian Processes; 1.8. Cross decomposition; 1.9. Naive Bayes; 1.10. Decision Trees; 1.11. Ensembles: Gradient boosting, random ...

7. www.datacamp.com › blog › supervised-machine-learningSupervised Machine Learning | DataCamp

   www.datacamp.com › blog › supervised-machine-learning

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   Supervised learning uses algorithms that learn the relationship of Features and Target from the dataset. This process is referred to as Training or Fitting. There are two types of supervised learning algorithms: Classification. Regression. Image Source: https://www.mathworks.com/help/stats/machine-learning-in-matlab.html.

8. developers.google.com › machine-learning › intro-toSupervised Learning | Machine Learning | Google for Developers

   developers.google.com › machine-learning › intro-to

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   May 14, 2024 · Supervised learning's tasks are well-defined and can be applied to a multitude of scenarios—like identifying spam or predicting precipitation. Foundational supervised learning concepts....