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.
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As a data scientist, when explaining partial correlation analysis with the consideration of covariates, you can describe it as a statistical technique that allows us to measure the direct relationship between two variables while accounting for the influence of other variables, known as covariates.
Typically, we are interested in understanding the relationship between a target variable and a specific predictor variable, but other factors can confound or influence this relationship. By performing partial correlation analysis with covariates, we can control for the effects of these other variables and isolate the direct association between the predictor of interest and the target variable.
This analysis helps us answer questions such as "What is the relationship between a particular predictor and the target variable when we account for the influence of other variables?" It provides a more precise and focused understanding of the relationship, as it removes the effects of other covariates that might be correlated with both the predictor and the target.
By considering the covariates, we can disentangle the direct impact of the predictor variable on the target variable from the potential confounding effects of other variables. This allows us to determine the unique contribution of the predictor, independent of the covariates.
The resulting partial correlation coefficient quantifies the strength and direction of the direct relationship between the predictor and the target, while controlling for the covariates. Additionally, the p-value associated with the partial correlation coefficient helps assess the statistical significance of this relationship.
Overall, partial correlation analysis with covariates is a powerful tool for data scientists to gain insights into the specific relationships between variables, enhance interpretability, perform feature selection, address multicollinearity, and investigate causal relationships in their machine-learning models. It allows us to uncover direct associations and refine our understanding of the underlying factors influencing the target variable.