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Q1
Discuss
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Type Of Machine Learning
What is machine learning?
S1.
Machine Learning is a field of computer science that uses statistical techniques to give computer system the ability to “learn” with data, without being explicitly programmed |
Q2
Discuss
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Type Of Machine Learning
What is Overfitting, and How Can You Avoid It?
S1.
Multiple way to avoid overfitting is - reguralization, bagging and boosting. |
Q3
Discuss
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Type Of Machine Learning
What is ‘training Set’ and ‘test Set’ in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?
S1.
The training set is data given to the model to analyze and learn and make computation. |
Q4
Discuss
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Type Of Machine Learning
How Can You Choose a Classifier Based on a Training Set Data Size?
S1.
When the training set is small, a model that has a high bias and low variance seems to work better because they are less likely to overfit. |
Q5
Discuss
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Type Of Machine Learning
What is Semi-supervised Machine Learning?
S1.
In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data. |
Q6
Discuss
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Type Of Machine Learning
What Is ‘naive’ in the Naive Bayes Classifier?
S1.
The classifier is called ‘naive’ because it makes assumptions that may or may not be correct. |
Q7
Discuss
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Type Of Machine Learning
How Will You Know Which Machine Learning Algorithm to Choose for Your Classification Problem?
S1.
While there is no fixed rule to choose an algorithm for a classification problem, you can follow these guidelines: |
Q8
Discuss
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Type Of Machine Learning
Considering a Long List of Machine Learning Algorithms, given a Data Set, How Do You Decide Which One to Use?
S1.
There is no master algorithm for all situations. Choosing an algorithm depends on the following questions: |
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