Simple Linear Regression
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 Coefficient of Slope: model.coef_ Coefficient of Intercept: model.intercept_
- Classification = Mode of the Target
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
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:Reference
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