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supervised learning

Innocent Iguana answered on April 5, 2022 Popularity 10/10 Helpfulness 2/10

answer supervised learning

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supervised learning

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- Machine Learning of labeled data
- Predict target values of unseen data, given the features
- Feature = predictor variable = independent variable
- Target variable = dependent variable = response variable
- 2 types:
    - Classification : Target is a category
    - Regression : Target is a continuous value
- Requirements for supervised learning
    - Must have no missing values
    - Data is numeric
    - X should be in 2D array, y should be in 1D array
    - Data is in array (for python, it is the numpy array)
    - Perform EDA to see if the data is formatted correctly
- Generic Workflow:
    1. Create a generic model
    2. Fit training data in the model
    3. Predict with the model for test data
- Some popular algorithms:
    - KNN 
    - Linear Regression
    - Ridge Regression (With regularization for large co-efficients)
    - Lasso Regression (With regularization for large co-efficients)
- Metric for  measuring model performance
    - accuracy, F1 score, Precision, Recall for classification
    - r-squared (percentage of explanability), RMSE (Average error) for regression

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Contributed on Feb 07 2023

Innocent Iguana

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supervised learning

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Supervised learning:
Supervised learning is a type of machine learning in which machine learn
from known datasets (set of training examples), and then predict the output.
A supervised learning agent needs to find out the function that matches a given sample set.
Supervised learning further can be classified into two categories of algorithms:

a- Classifications
b- Regression

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Contributed on Apr 05 2022

BlueMoon

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supervised learning

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