Ways to improve your model accuracy
Boost your model accuracy with these simple steps
Model accuracy in machine learning refers to the measurements used to determine whether or not a particular model is the best to explain the relationship between the many problem variables. To train a model for new, unused data, we frequently employ training data.
Why model accuracy is important
If our model is accurate, it will perform well on both the training and new data. The accuracy of the model is very significant from the user’s perspective because it not only improves the model but also helps users.
Ways to improve your model accuracy
There are some ways to improve the accuracy of your models.
Hyperparameter tuning
The task of selecting a set of ideal hyperparameters for a learning algorithm is known as hyperparameter optimization. A hyperparameter is a parameter whose value governs the learning process. Other parameters (usually node weights) are learned in contrast.
Data cleaning
The practise of fixing or deleting incorrect, corrupted, improperly formatted, duplicate, or incomplete data from a dataset is known as data cleaning. By merging multiple data sources, data can be duplicated or improperly categorised in a variety of ways.
Cross validation
Cross-validation is a resampling method that tests and trains a model on different iterations using different chunks of the data. It is most commonly employed in situations when the goal is prediction and the user want to assess how well a predictive model will perform in practise.
Quality over quantity
Always prioritise data quality above data quantity. Having more data can pose various problems, and it does not always mean that having more data is better for the model because having more data can cause overfitting in some circumstances.
Use different Algorithms
If an algorithm does not provide good accuracy, consider several models because certain data may perform better on different models depending on the data.
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Conclusion
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