implementing Lasso regression with train-validation-test split and finding the optimal regularization parameter. In Python, it involves splitting the data, training Lasso model with different alpha values, finding the best alpha, retraining the model, and evaluating on the test set. In R, it includes data splitting, training Lasso models, finding the best lambda, retraining, and testing.
The training-validation-test split involves using the training set to fit the model, the validation set to tune hyperparameters, and the test set to evaluate performance. Python’s scikit-learn library can be used for this process, ensuring the model generalizes well to new data by evaluating it on unseen data and avoiding overfitting.
Underfitting in machine learning occurs when a model fails to capture underlying data patterns due to simplicity or insufficient training data. To address underfitting, select complex models, add features, and obtain more training data. Also, fine-tune hyperparameters and optimize the model’s architecture. Few features in a model can also cause underfitting, requiring the identification of relevant additional features or more advanced modeling techniques.
This comic explains MSE and MAE, the commonly used evaluation metrics for regression. MSE emphasizes large deviations, while MAE provides a more robust measure when outliers are less significant. MSE is preferred as a loss function due to its ability to penalize larger errors more heavily and its suitability for mathematical optimization, stability, and statistical interpretation. RMSE is the square root of MSE and also penalizes large errors.
Machine learning parameters are values learned from training data to minimize prediction errors. For example, in a uniform distribution for bus arrival times, parameters $latex a$ and $latex b$ define the range. They are the model’s knobs for accurate predictions.
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. In unsupervised learning, the goal is to infer the natural structure present within… Unsupervised learning helps detect shady people
Comments: I already asked my student, and he confirmed that the reason he studied the ML class was because there was a model in that class ?. So, Mr. Fox left the class after he… The model that’s not a girl & time machine
Supervised learning involves training an algorithm on labeled data and pairing input with correct output. Unsupervised learning uses unlabeled data to find patterns. For example, predicting pizza delivery tips involves features like time, pizza type, distance, and tip history, with the goal of predicting tip outcomes.
After collecting and preprocessing the dataset, it is essential to divide it into two distinct sets: training set and testing set. The training set is used to train the model while the testing set is used to evaluate its performance. This allows assessment of the model’s generalization to new data. Two code examples in Python and R demonstrate how to create synthetic data and split it into training and testing sets using popular libraries.
Model generalization in machine learning is a crucial concept that refers to the ability of a trained model to perform well on new, unseen data. When a model generalizes well, it demonstrates an understanding of… A comic guide to model generalization and overfitting