"Your model is underfitting the training data when the model performs poorly on the training data. By doing this, it loses its generalization power, which leads to poor performance on new data. Overfitting tends to make the model very complex by having too many parameters. "In machine learning, overfitting occurs when a learning model customizes itself too much to describe the relationship between training data and the labels. A model that is under-fitted does not match closely enough. A model that is overfitted matches the data too closely. A well-fitted model produces more accurate outcomes. "Model fitting is a measure of how well a machine learning model generalizes to similar data to that on which it was trained. Balancing bias and variance can solve overfitting and underfitting.īullseye Diagram: The distribution of model predictions And, model fitting depends on the Bias-Variance Tradeoff in machine learning. Accuracy is affected by your model fitting. But, what factors affect model accuracy?Īccuracy is the percentage of correct predictions that a trained ML model makes. We also claimed that “Evaluating the accuracy of a machine learning model is critical in selecting and deploying a machine learning model.” In the article “Which Machine Learning (ML) to choose? ”, which helps you to choose the right ML for your data, we indicated that “From a business perspective, two of the most significant measurements are accuracy and interpretability.”
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