Reference Model

  • A prototype model that displays the most basic performance that becomes a reference for the prediction model
  • Types:
    - Classification = Mode of the Target
    • Regression = Mean of the Target
    • Time-Series Regression = The value of the prior time-stamp

Regression Line/Shape

  • Residual = The difference between predicted value and observed value

  • Error = The difference between predicted value and true value (of population)

  • Line of Regression = Residual Sum of Squares (RSS) -- the line that minimizes RSS (also called Sum of Squared Errors)

  • Least Squared Method = Used to find the slope/intercept of the linear regression

Variables:

  • x = independent variable/feature
  • y = dependent variable/target

Linear Regression Model using Scikit-learn

from sklearn.linear_model import Linear Regression

model = LinearRegression()

feature = [x]
target = [y]
X_train = df[feature]
y_train = df[target]

model.fit(X_train, y_train)

X_test = [test] #example
y_pred = model.predict(X_test)

y_pred

Coefficients:

  • Coefficient of Slope: model.coef_
  • Coefficient of Intercept: model.intercept_
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a Philosopher aspiring to become an AI/ML/DL Engineer and Data Scientist.

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