What is overfitting, and how can you prevent it?

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Khushi Singh · Answer

The practice of 
overfitting occurs frequently in machine learning systems which improperly learn training data details such as noise and outliers making generalization to new data ineffective. The model becomes overfit when it contains an impractically complex structure which has more parameters than available data points. A model that masters training data demonstrates poor performance for predicting validation and test sets.
The phenomenon of 
overfitting leads to high training results yet it produces significant reductions in accuracy when validating or testing data. Memorization of training data occurs instead of pattern detection because the model uses training data as its main basis defeating the goal of predictive modeling.
The prevention of overfitting requires these techniques to be used:
The data should be divided into training and validation subsets multiple times to validate that the model demonstrates strong performance across each split.
and Algorithms with fewer parameters should be selected for modeling purposes or the model complexity should be minimized.
and Lasso and Ridge regularization methods both implement penalty terms that prevent models from becoming too complex by adding to the loss function.
and Decision tree pruning reduces the ineffective sections of a model which cannot predict the target variable.
and Early stopping invokes an automatic termination of model training as validation set results begin to decrease.
and During training dropout for neural networks sets random neurons to inactive status which prevents the network from becoming dependent on particular inputs.
and Excessive training data helps the model perform generalization more accurately while preventing it from memorizing patterns.
Overfitting leads to lowered predictive strength because it affects how a model works on genuine real-world data. The optimal model requires finding the correct level of complexity where it fits between basic models that are too simple and complex models that are too detailed while maintaining reliable performance on unknown situations.
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What is overfitting, and how can you prevent it?

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