Master the Bias/Variance Trade-off in KNN Algorithm

Cross-Validation

• Stratified cross-validation is an extension of normal cross-validation that fixes the problem of information leakage and significant bias.
• Walk-forward optimization is suitable for time series problems.
• Stratified cross-validation is recommended for datasets with target imbalance problem.

KNN Algorithm

• In KNN, as parameter k increases, bias decreases, and variance increases.
• Weighing neighbors in KNN is used to give more importance to closer neighbors.
• Distance metrics in KNN can introduce the problem of feature dominance.
• Feature scaling is necessary in KNN because it is sensitive to the magnitude of features.
• The curse of dimensionality problem in KNN occurs when there are too many features, making it difficult to define a meaningful distance metric.
• A good approach to solving the multidimensionality problem in KNN is to use dimensionality reduction techniques.
• Two popular algorithms for making the search process more efficient in KNN are Ball Tree and KD Tree.
• The KNN model is sensitive to the choice of distance metric and the value of k.
• A good solution to the problem of insignificance in features in KNN is to use feature selection or feature engineering.
• The main advantage of K-nearest neighbours algorithm is that it is simple to implement and can handle nonlinear boundaries.

Support Vector Machines

• The main goal of Support Vector Machines is to find the hyperplane that maximally separates the classes.
• The main advantage of Support Vector Machines is that they can handle high-dimensional data and are robust to outliers.
• Professor Vladimir Vapnik made significant contributions to the development of Support Vector Machines, including the introduction of the soft margin and the kernel trick.

KNN Model

• Three key hyperparameters for the KNN model are the number of neighbors (k), the distance metric, and the weighting scheme.
• The main difference between the regression version and the classification approach in KNN is that regression predicts continuous values, while classification predicts categorical values.
• A rule of thumb for choosing the number of k neighbors in KNN is to start with a small value and increase it until the performance plateaus.
• The purpose of distance metrics in KNN is to measure the similarity between data points.
• The most popular distance metric used in KNN is Euclidean distance.
• The K-nearest neighbors (KNN) algorithm is a simple, non-parametric algorithm that classifies a new instance by finding the k most similar instances in the training set.

Cross-Validation and Miscellaneous

• The purpose of the outer loop in cross-validation is to evaluate the performance of the model on unseen data.
• The purpose of the inner loop in cross-validation is to tune the hyperparameters of the model.
• The MLU-Explain course is made available under the Creative Commons Attribution 4.0 International License.
• Parametric algorithms make assumptions about the distribution of data, while non-parametric algorithms do not make any assumptions.