Python Sklearn ML Machine Learning Guided Project Error analysis model improvement partial correlati
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Predicting the health of power transformers using machine learning is crucial for ensuring the reliable operation of electrical grids. Will be analyzing historical data on transformer performance found here on Kaggle, machine learning algorithms can identify patterns and anomalies that indicate potential failures or deteriorating conditions. This early detection enables proactive maintenance and replacement, minimizing downtime, reducing costs, and preventing catastrophic failures that can lead to power outages.
Pingouin's partial correlation analysis is a powerful tool for assessing the significance of a correlation between a target variable and predictor variables while controlling for the influence of other features. By accounting for the confounding effects of covariates, partial correlation analysis helps elucidate the unique contribution of each predictor to the target variable. The resulting p-value provides valuable insights into whether the observed correlation is statistically significant, considering the controlled variables' impact.
For preprocessing we have many exponential distribution so we opt to do a log transformation, using numpy log, on many columns. But here we have some values that are 0 which creates an inf value and will create an error as we start to train our model. We'll show you how we dealt with this common problem when completely a log transformation in your data preprocessing step to get your distributions ready for your ML model.
In this project we choose to test a diverse set of ML models in sklearn. Here we use ARDRegression and KNeighborsRegressor as the base model in a BaggingRegressor ensemble method. We also test the Gradient Boosting Regressor and the Random Forest Regressor in this guided Python project.
We use these models in a Bayesian grid search to enhance the optimization process by leveraging prior knowledge and incorporating uncertainty estimation. By using a probabilistic approach, Bayesian grid search explores the parameter space more efficiently and effectively than traditional grid searches. It allows for a more informed decision-making process by providing posterior distributions and credible intervals, enabling a deeper understanding of parameter sensitivities and trade-offs.
Performing error analysis after identifying the best hyperparameters is a crucial step to gain insights into the model's weaknesses and identifying areas for improvement. By thoroughly examining the errors, such as misclassifications or inaccurate predictions, patterns, and common pitfalls can be identified. This analysis can guide feature engineering efforts, enabling the inclusion of new or refined features that specifically address the identified error sources, ultimately enhancing the model's predictive capabilities. After our grid search, we have an ok R-squared of .70 but after the error analysis, we achieve a .99 on the test data set. This incredibly high R-squared is probably my best score in a regression problem. Error analysis doesn't always help so much but it does help and this highlights how powerful it can be.
