Give the pictorial process for a supervised learning problem.
Supervised Learning Problem.
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| Give the pictorial process for a supervised learning problem. |
What is supervised learning?
Supervised learning is when we are teaching a machine to learn using inputs that we know the correct outputs to.
What is unsupervised learning?
Unsupervised learning is when we are teaching a machine to learn using inputs that we do not know the correct outputs to. We can derive the structure of our given data by clustering it based upon relations among the given data.
What are two different types of supervised learning problems?
- Regression Problems
- Classification Problems
What is a Regression problem ?
A regression problem is a supervised machine learning problem in which we are trying to map inputs to a continuous function.
What is a Classification problem?
A classification problem is a supervised machine learning problem in which we are trying to map inputs to discrete outputs.
What are the necessary to develop a learning algorithm for a supervised machine learning problem?
1. Obtain the data set.
2. Feed the training set to the learning algorithm we have created.
3.The hypothesis takes an input and tries to output the estimated value of our output Y.
What is the purpose of our hypothesis in a supervised regression problem?
The purpose of our hypothesis in a supervised regression problem is to take an input and try to return the estimated value of our output y.
Learn More :
Machine Learning
- Give a popular application of machine learning that you see on a day-to-day basis?
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- What is algorithm independent machine learning?
- Explain what is the function of 'Supervised Learning'?
- What is the function of unsupervised learning?
- What is 'Training set' and 'Test set'?
- What is the standard approach to supervised learning?
- What are the three stages to build the hypotheses or model in machine learning?
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- What are the five popular algorithms of Machine Learning?
- What is inductive Machine Learning?
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- Why does overfitting happen?
- What is 'Overfitting' in Machine learning?
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- What is Machine Learning?
- Give a derivation of for a single example in batch gradient descent? (Gradient Descent For Linear Regression)
- What is the algorithm for implementing gradient descent for linear regression?
- How does gradient descent converge with a fixed step size alpha?
- Why should we adjust the parameter alpha when using gradient descent?
- Why does gradient descent, regardless of the slope's sign, eventually converge to its minimum value?