7  FAQ - Classification

Q: What is a Classification Model?

A: A classification model is a type of machine learning model used for predicting the category or class of a given input. It assigns a class label to input data based on learned patterns from training data. Examples include email spam detection (spam or not spam) and medical diagnoses (sick or healthy).

Q: How Do I Choose the Right Algorithm for My Classification Problem?

A: Start with simple models like Logistic Regression or Decision Trees to establish a baseline. Consider the nature of your data, the problem complexity, and computational resources. Experiment with more complex models (e.g., Random Forests, SVMs) and use cross-validation to compare their performance based on relevant metrics.

Q: What’s the Difference Between Binary and Multi-class Classification?

A: Binary classification involves distinguishing between two classes (e.g., yes or no), while multi-class classification involves categorising inputs into more than two classes (e.g., types of fruits, dog breeds).

Q: How Do I Handle Imbalanced Classes in My Dataset?

A: Use techniques like oversampling the minority class, undersampling the majority class, or generating synthetic samples (SMOTE). Adjusting the class weight parameter in the model can also help.

Q: What Metrics Should I Use to Evaluate My Classification Model?

A: Common metrics include accuracy, precision, recall, F1 score, and the area under the ROC curve (AUC-ROC). Choose metrics that best reflect the importance of false positives and false negatives in your context.

Q: How Do I Deal with Overfitting?

A: Regularisation techniques, pruning decision trees, or limiting the depth of trees in ensemble methods can reduce overfitting. Cross-validation helps ensure that the model generalises well to unseen data.

Q: Can I Use Categorical Data Directly in My Model?

A: Most models require numerical input. Convert categorical data into numeric form using methods like one-hot encoding or label encoding before training your model.

Q: What is Hyperparameter Tuning and How Do I Do It?

A: Hyperparameter tuning involves finding the optimal configuration of model parameters that aren’t learned from data. Use techniques like Grid Search or Random Search to systematically explore different combinations of parameters.

Q: How Important is Feature Selection?

A: Very. Selecting the right features can improve model performance and reduce computation time. Use techniques like feature importance scores from models to identify and select the most relevant features.

Q: What is Cross-Validation and Why Should I Use It?

A: Cross-validation is a technique for assessing how the results of a statistical analysis will generalise to an independent dataset. It involves partitioning the data into subsets, training the model on some subsets (training set) and testing it on others (validation set). This method helps solve the problem of overfitting and provides a more accurate measure of model performance.

Q: What Should I Do If My Model Is Not Performing Well?

A: Revisit each step of the model-building process: data cleaning, feature selection, model choice, and hyperparameter tuning. Consider whether you might need more data or if feature engineering could uncover more informative attributes. Sometimes, collecting additional features or more samples can significantly improve performance.