Predictive Maintenance: Predicting Machine Failure using Sensor Data with XGBoost and Python
Predictive maintenance is a game-changer for the modern industry. Still, it is based on a simple idea: By using machine … Read more
Cross-Validation
Here you’ll find everything about cross-validation, whether it’s Python tutorials on implementing cross-validation with Python or conceptual articles describing different types.
Cross-validation is a technique for evaluating the performance of a machine-learning model. It is an essential step in the model development process. When we cross-validate a model, this involves splitting the training dataset into multiple subsets, called folds. We then train the model on one subset and evaluate it on the remaining subsets. This is done for each possible combination of training and evaluation subsets. The performance of the model is averaged across all of the splits. This allows us to train a model and evaluate it on different data. The result is a more accurate estimate of the model performance. Evaluating a model on multiple folds can provide a more accurate estimate of the model’s performance than using a single split of the data. This can be useful for choosing the best model or for fine-tuning model hyperparameters. It also helps to avoid overfitting.
Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python
Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with … Read more