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1. Top results related to supervised learning

   • supervised learning,unsupervised learning ,regression

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     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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     Supervised Machine LearningSupervised learning is a machine learning technique that is widely used in various fields such as finance, healthcare, marketing, and more. It is a form of machine learning in which the algorithm is trained on labeled data to make predictions or decisions based on the data inputs.In supervised learning, the algorithm learns a mapping between the input and output data. This mapping is learned from a labeled dataset, which consists...

     • Figure A: It is a dataset of a shopping store that is useful in predicting whether a customer will purchase a particular product under consideration or not based on his/ her gender, age, and salary. Input: Gender, Age, Salary Output: Purchased i.e. 0 or 1; 1 means yes the customer will purchase and 0 means that the customer won’t purchase it. 
     • Figure B: It is a Meteorological dataset that serves the purpose of predicting wind speed based on different parameters. Input: Dew Point, Temperature, Pressure, Relative Humidity, Wind Direction Output: Wind Speed 
     • Linear Regression: Linear regression is a type of regression algorithm that is used to predict a continuous output value. It is one of the simplest and most widely used algorithms in supervised learning. In linear regression, the algorithm tries to find a linear relationship between the input features and the output value. The output value is predicted based on the weighted sum of the input features.
     • Logistic Regression: Logistic regression is a type of classification algorithm that is used to predict a binary output variable. It is commonly used in machine learning applications where the output variable is either true or false, such as in fraud detection or spam filtering. In logistic regression, the algorithm tries to find a linear relationship between the input features and the output variable. The output variable is then transformed using a logistic function to produce a probability value between 0 and 1.
     • Decision Trees: Decision tree is a tree-like structure that is used to model decisions and their possible consequences. Each internal node in the tree represents a decision, while each leaf node represents a possible outcome. Decision trees can be used to model complex relationships between input features and output variables. A decision tree is a type of algorithm that is used for both classification and regression tasks.
     • Decision Trees Regression: Decision Trees can be utilized for regression tasks by predicting the value linked with a leaf node.
     • Decision Trees Classification: Random Forest is a machine learning algorithm that uses multiple decision trees to improve classification and prevent overfitting.
     • Labeled training data benefits supervised learning by enabling models to accurately learn patterns and relationships between inputs and outputs.
     • Supervised learning models can accurately predict and classify new data.
     • Supervised learning has a wide range of applications, including classification, regression, and even more complex problems like image recognition and natural language processing.
     • Well-established evaluation metrics, including accuracy, precision, recall, and F1-score, facilitate the assessment of supervised learning model performance.
     • Overfitting: Models can overfit training data, which leads to poor performance on new, unseen data due to the capture of noise.
     • Feature Engineering: Extracting relevant features from raw data is crucial for model performance, but this process can be time-consuming and may require domain expertise.
     • Bias in Models: Training data biases can lead to unfair predictions.
     • Supervised learning heavily depends on labeled training data, which can be costly, time-consuming, and may require domain expertise. 

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   • Supervised Machine Learning Examples

     Supervised machine learning technology is a key in the world of the dramatic innovations of the modern AI. It is applied in numerous items, such as coat the email and the complicated one, self-driving carsOne of the most important tasks when it comes to supervised machine learning is making computers guess or choose by looking at the data. The article is set to figure out how supervised machine learning works, talk about the case studies from different domains, and answer to the common questions about its potential.

     How Supervised Machine Learning Works?

     Supervised machine learning is kind of like teaching a child using examples. Just as a child learns to tell different things apart by looking at labeled examples, supervised learning algorithms learn to make predictions or categorize data by looking at pairs of inputs and outputs. Here’s how it works: you give a machine learning model many examples with inputs and their corresponding labels. Then, during the training phase, the model figures out the patterns or connections between the inputs and outputs. Once it’s trained, it can use those patterns to predict...

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

   en.wikipedia.org › wiki › Supervised_learning

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   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 human-labeled supervisory signal) train a model.

3. www.geeksforgeeks.org › supervised-unsupervisedSupervised and Unsupervised learning - GeeksforGeeks

   www.geeksforgeeks.org › supervised-unsupervised

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   Mar 13, 2024 · Supervised and unsupervised learning are two fundamental approaches to machine learning that differ in their training data and learning objectives. Supervised learning involves training a machine learning model on a labeled dataset, where each data point has a corresponding label or output value.

4. 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.

5. cloud.google.com › what-is-supervised-learningWhat is Supervised Learning? | Google Cloud

   cloud.google.com › what-is-supervised-learning

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   Supervised learning is a category of machine learning and AI that uses labeled datasets to train algorithms to predict outcomes. Learn more.

6. 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 · In supervised learning, a model is the complex collection of numbers that define the mathematical relationship from specific input feature patterns to specific output label values. The model...

7. builtin.com › machine-learning › supervised-learningWhat Is Supervised Learning? (Definition, Examples) | Built In

   builtin.com › machine-learning › supervised-learning

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   Jan 3, 2023 · Supervised learning is an approach to machine learning that uses labeled data sets to train algorithms in order to properly classify data and predict outcomes.

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

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

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   Discover what supervised machine learning is, how it compares to unsupervised machine learning and how some essential supervised machine learning algorithms work