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Several additional approaches were attempted but deprioritized or entirely eliminated from the final workflow due to lack of positive impact on the validation MAE. Summary of approach: Our solution for Phase 1 is a gradient boosted decisiontree approach with a lot of feature engineering.
DecisionTreesDecisiontrees recursively partition data into subsets based on the most significant attribute values. Python’s Scikit-learn provides easy-to-use interfaces for constructing decisiontree classifiers and regressors, enabling intuitive model visualisation and interpretation.
For example, linear regression is typically used to predict continuous variables, while decisiontrees are great for classification and regression tasks. Decisiontrees are easy to interpret but prone to overfitting. predicting house prices), Linear Regression, DecisionTrees, or Random Forests could be good choices.
DecisionTrees These trees split data into branches based on feature values, providing clear decision rules. Cloud platforms like AWS , Google Cloud Platform (GCP), and Microsoft Azure provide managed services for Machine Learning, offering tools for model training, storage, and inference at scale.
It offers implementations of various machine learning algorithms, including linear and logistic regression , decisiontrees , random forests , support vector machines , clustering algorithms , and more. There is no licensing cost for Scikit-learn, you can create and use different ML models with Scikit-learn for free.
Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. What are the advantages and disadvantages of decisiontrees ? Have you worked with cloud-based data platforms like AWS, Google Cloud, or Azure? In my previous role, we had a project with a tight deadline.
The weak models can be trained using techniques such as decisiontrees or neural networks, and the outputs are combined using techniques such as weighted averaging or gradient boosting. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.
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