2031, Cross Validation and Data Analysis

Must-Have Skills for a Machine Learning Engineer

Pickl AI

NOVEMBER 28, 2024

billion by 2031, growing at a CAGR of 34.20%. Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data. Validation strategies, such as cross-validation, help assess a model’s generalisation ability and prevent overfitting.

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Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

billion by 2031 at a CAGR of 34.20%. Cross-Validation: Instead of using a single train-test split, cross-validation involves dividing the data into multiple folds and training the model on each fold. This technique helps ensure that the model generalises well across different subsets of the data.

Machine Learning

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Must-Have Skills for a Machine Learning Engineer

Understanding and Building Machine Learning Models

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