Nama : Sultan Chairul
NIM : 2581484737
Assigement 4
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Explain the goal of linear regression using the equation $y = mx + b$. What do $m$ and $b$ represent?
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What is the primary difference in the output of a regression model versus a classification model?
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How does a decision tree make a prediction?
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Give an example of a business problem that could be solved with classification (e.g., spam detection)
Answer :
1. Explain the goal of linear regression using the equation \(y = mx + b\). What do \(m\) and \(b\) represent?
Linear regression aims to predict a continuous value by finding the relationship between variables. In the equation \(y = mx + b\), \(m\) represents the slope of the line, while \(b\) represents the y-intercept, or the value of \(y\) when \(x = 0\).
2. What is the primary difference in the output of a regression model versus a classification model?
A regression model predicts a continuous numerical value, such as house prices. A classification model predicts a category or class, such as whether an email is spam or not.
3. How does a decision tree make a prediction?
A decision tree makes predictions by following a series of questions or conditions based on input features. It moves through branches until it reaches a final leaf node, which provides the prediction.
4. Give an example of a business problem that could be solved with classification (e.g., spam detection).
A business can use classification to predict whether a customer will cancel their subscription based on their activity and purchase history. This helps the company take action to retain customers.
Keterangan : 100%
Bukti : Sudah mengerjakan tigas dengan baik dan benar.
Terimakasih.