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We usually talk about two main types of machine learning models:
• A Classification model
• A Regression model
They are different, and it's essential to understand why.
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• A Classification model
• A Regression model
They are different, and it's essential to understand why.
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Whenever the result of your predictions is categorical, you have a classification model.
For example, when your prediction is a binary value (True or False,) or when you want to predict a specific animal from a picture (Lion, Zebra, Horse.)
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For example, when your prediction is a binary value (True or False,) or when you want to predict a specific animal from a picture (Lion, Zebra, Horse.)
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If the result of your predictions is numerical, you have a regression model.
For example, returning a stock's future price, the value of a house, or tomorrow's temperature.
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For example, returning a stock's future price, the value of a house, or tomorrow's temperature.
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Be aware that "Logistic Regression" is a classification model, not a regression one!
I know. Dumb naming. But it is what it is.
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I know. Dumb naming. But it is what it is.
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A few examples of problems so you can determine whether they are classification or regression:
1. Predicting the age of a person
2. Predicting the nationality of a person
3. Predicting whether an email is spam or not
4. Predicting the total revenue from a product
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1. Predicting the age of a person
2. Predicting the nationality of a person
3. Predicting whether an email is spam or not
4. Predicting the total revenue from a product
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5. Predicting the number of products sold
6. Predicting whether it will rain tomorrow
7. Predicting how many inches will snow tomorrow
Answers: RCCRRCR
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6. Predicting whether it will rain tomorrow
7. Predicting how many inches will snow tomorrow
Answers: RCCRRCR
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