Prediction of Dental Implants Using Machine Learning Algorithms
Algorithm 1
Improved AdaBoost pseudocode.
(i)
Data preprocessing for AdaBoost
(i)
Make data ready by loading it from the dataset
(ii)
Split dataset into input list of variable X and target variable y that correspond to the class we have to predict.
(iii)
Split the dataset into training and testing datasets. Here, we split our dataset following the common scenario of 70% for training and 30% for testing.
(ii)
From the first iteration k = 1 to a certain fixed number of iterations, each training example in the dataset is weighted. Here, the initialization of the sample weights is completed by considering a new random sample set of values.
(i)
When the maximum specified number of base learners is not reached:
(i)
Add a new base learner
(ii)
Train the base learner with a weighted sample
(iii)
Make the predictions over all data points in the dataset
(iv)
Calculate the prediction error rate
(v)
Update the weight values for the training instances based on prediction results
(vi)
Normalize the updated sample weights so that they add up to 1
End while
(ii)
Make the output predictions of the kth iteration
(iii)
Keep the higher prediction accuracy obtained until the current iteration k
(iii)
End For
(iv)
Consider the final output prediction, which is the higher prediction accuracy obtained over all iterations
