## Linear Regression

• A simple yet useful supervised learning approac for predicting quantitative (numeric) response
• Makes prediction by simply computing a weighted sum of input feature, plus a contst called as bias term.
• Finding Parameters
• Choose parameters in a way so that prediction is close to actual values for the training samples
• Define a cost function to find the parameters that minimize the cost function => MSE (Mean squared Error)
• Some methods: Least Squares Method, Gradient Descent
• Main Steps:
• Use least-squares to fit a line to the data
• Calculate R-Squared
• Calculate p-value
• Terminology:
• R-Squared: a goodness of fit measure for linear regression modesl
• Null Hypothesis: An initial statement claiming that there is no relationship between two measured events
• P-Value: Tests the null hypothesis
• Low p-value (`< 0.05`): Null hypothesis can be rejected
• Predictor likelya meaningful addition to your model
• Changes in the predictor’s values are related to changes in the response variable
• Large p-value: Suggest that predictor not associated with changes in response

## Tidymodels steps

• Split data ({rsample})
• prepare recipe ({recipes})
• Specify model ({parsnip})
• Tune hyperparameters ({tune})
• Fit model ({parsnip})
• Analyze model ({broom})
• Predict ({parsnip})
• Interpret the results ({yardstick})

## Linear Regression with known dataset diamonds

• Lets build the price predictor    • Now lets find all the columns which have high correlation with price • Now lets split the training and testing data from this data • Now lets use `lm` to create the model for the training data • broom package has a method to summarize models in a way they are easy to read `broom::tidy(model)` • According to work which we have done so far carat, x, y, z can be used to predict the price of diamond, Lets use all the variables and see the results, y and z will be insignificant if we consider all variables  This site uses Akismet to reduce spam. Learn how your comment data is processed. ## About continuous learner

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