Machine Learning

4 Methods Of Building Machine Learning Model

In this article, we have explain 4 methods of building machine learning model.

4 methods of building machine learning model

1. Backward Elimination
2. Forward Selection
3. Bidirectional Elimination
4. All Possible Models

1. Backward Elimination

Step 1
Select a significance level to stay in the model (eg. SL = 0.05).

Step 2
Fit the full model with all possible predictors.

Step 3
Consider the predictor with the higher P - value. If P > SL, go to STEP 4 otherwise go to FIN.

Step 4
Remove the predictor.

Step 5
Fit model without this variable.

FIN: Your model is read.

2. Forward Selection

Step 1
Select a significance level to enter the model (e.g. SL=0.05).

Step 2
Fit all simple regression model y~Xn select the one with the lowest P - value.

Step 3
Keep this variable and fit all possible models with one extra predictor added to the one(s) you already have.

Step 4
P - value. If P >SL, go to STEP 3, otherwise go to FIN.

FIN: Keep the previous model.

3. Bidirectional Elimination

Step 1
Select a significance level to enter and to stay in the model e.g. SLENTER = 0.05, SLSTAY = 0.05.

Step 2
Perform the next step of forward selection (next variable must have P < SLENTER to enter).

Step 3
Perform ALL steps of Backward Elimination (old variable must have P < SLSTAY to stay).

Step 4
No new variable can enter and no old variable can exit.

FIN: Your model is ready.

4. All possible Models

Step 1
Select a criterion of goodness of fit (e.g. Akaike criterion).

Step 2
Construct all possible regression models: 2N - 1 total combinations.

Step 3
Select the one with the best  criterion.

FIN: Your model is ready.

In this article, we have seen 4 methods of building machine learning model.

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