Data cleaning is the utmost import step! I had to go through each sample individually because things are missing or a mess like 50% of the time, this takes forever.
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More diversity in the data set leads to improved performance of the given model with increasing volume of data. A lot of data scientists synthesize datasets using GANs to achieve better learning.
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It helps in improving the models prediction by refining the features which is called feature engineering. That means we need to realise how important they are if we are to decide rightly.
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Cross-validation ensures the model performance across different subsets of data, prevents overfitting, and highlights stability problems.
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Model performance can be constrained by default parameters. It achieves 85% accuracy and has a potential to reach 92% accuracy with hyperparameter optimization.
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To fit your data, model selection is key. Use the simple as well as advanced algorithms for optimal performance.
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Model ensembling improves performance by combining models. But the mixing of low and high performers enhances the precision.
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