reading-notes

Linear Regressions

Notes

Reading Questions

  1. Can you explain the basic concept of linear regression and its purpose in the context of machine learning and data analysis?

    Linear regression plots scattered data points and estimates the linear relationship between those points to provide a model for predicting future points in the data. A straight line that best fits the linear relationship of the data is added to the plotted to visualize this. It is used in the context of machine learning and data analysis to analyze the if/what the correlation between variables in a large data set and predict output.

  2. Describe the process of implementing a linear regression model using Python’s Scikit Learn library, including the necessary steps and functions.

    1. Import

       import numpy as np
 from sklearn.linear_model import LinearRegression

    2. Data

       # x is input, y is output
 x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1))
 y = np.array([5, 20, 14, 32, 22, 38])

    3. Model

       model = LinearRegression()
 model.fit(x, y)
 ## OR ##
 model = LinearRegression().fit(x, y)

    4. Results

       r_sq = model.score(x, y)

    5. Predict

       y_pred = model.predict(x)

    Code samples from Real Python

  3. What is the purpose of splitting the dataset into train and test sets, and how does this contribute to the evaluation of a machine learning model’s performance?

    The train test split allows you to model a simulation of how a model would perform with new data. By splitting the data into a train set and a test set it provides a way to tests the models performance with new data.

Things I want to know more about

References