**What is K-Nearest Neighbors (KNN)?**

KNN is a straightforward, non-parametric, and supervised algorithm in machine learning that is employed for classification and regression purposes. It predicts outcomes by considering either the most prevalent class or the mean value of the k-nearest data points.

**How does KNN classify data points?**

KNN categorizes a data point by locating the k-nearest neighbors in the feature space and allocating the most frequent class label within that neighborhood (for classification).

**What is the significance of ‘k’ in KNN?**

The variable ‘k’ indicates the number of closest neighbors taken into account during prediction. The selection of ‘k’ impacts the level of smoothness and responsiveness of the model.

**Explain the difference between KNN and other classification algorithms like Decision Trees.**

KNN predicts outcomes by considering the closeness of data points, whereas Decision Trees employ a hierarchical set of rules for prediction. Unlike Decision Trees, KNN is non-parametric and does not need a training phase.

**What is the Euclidean distance in the context of KNN?**

Euclidean distance measures the direct distance between two points in space and is frequently utilized in KNN to calculate the distance between data points.

**How does KNN handle categorical data?**

KNN has the capability to process categorical data by employing suitable distance measurements like Hamming distance, which is specifically designed for categorical characteristics.

**What is the curse of dimensionality, and how does it affect KNN?**

The curse of dimensionality pertains to difficulties that arise when handling data with a high number of dimensions. In the KNN algorithm, when the number of dimensions increases, the significance of the distance between points diminishes, which negatively affects the algorithm’s effectiveness.

**What is the role of normalization in KNN?**

In KNN, it is necessary to normalize the data to ensure fairness in the contribution of all features to the distance calculation. This prevents features with larger scales from overpowering the distance metric.

**Explain the concept of the decision boundary in KNN.**

The KNN decision boundary is the line that distinguishes various classes and can be established based on the arrangement of the k-nearest neighbors within the feature space.

**What are the advantages of KNN?**

KNN is straightforward, simple to execute, and efficient for datasets of moderate size. It is capable of solving classification problems for both binary and multiclass scenarios.

**What are the limitations of KNN?**

KNN can be time-consuming in terms of computation, particularly when dealing with extensive datasets. It is prone to outliers and can be influenced by irrelevant or redundant features.

**How does KNN handle imbalanced datasets?**

KNN might encounter difficulties when dealing with imbalanced datasets as it has a tendency to prioritize the majority class. To overcome this problem, methods such as modifying class weights or adopting resampling techniques can be employed.

What is the impact of the choice of ‘k’ on the bias-variance tradeoff in KNN?

Decreasing the values of ‘k’ results in a model that is more adaptable with a greater variance and reduced bias, whereas increasing the values of ‘k’ creates a smoother model with reduced variance and increased bias.

**Explain the process of choosing the optimal ‘k’ value in KNN.**

The most suitable value for ‘k’ is frequently determined using methods such as cross-validation, where various ‘k’ values are evaluated, and the one that exhibits the highest performance on unfamiliar data is selected.

**How does KNN handle missing values?**

KNN has the capability to deal with missing values by substituting them with the average or median value of the feature obtained from its closest neighbors.

**Can KNN be used for regression tasks?**

Certainly, regression tasks can utilize the K-nearest neighbors algorithm (KNN) where the prediction entails determining the average value of the target variable amidst the k-most similar neighboring instances.

**What is the impact of outliers on KNN?**

The utilization of distances in the KNN algorithm makes it susceptible to the influence of outliers. Outliers have the potential to distort proximity relationships, resulting in less precise predictions.

**How does KNN perform on high-dimensional data compared to low-dimensional data?**

KNN generally exhibits superior performance on low-dimensional data due to the decrease in significance of the distance metric in high-dimensional spaces, resulting in a decline in performance.

**What is the role of the ‘weight’ parameter in KNN?**

The KNN’s ‘weight’ parameter allows for assigning varying weights to neighbors during prediction. Popular choices include ‘uniform’ (equal weights) and ‘distance’ (reciprocal of the distance).

**In what scenarios would you prefer to use KNN over other algorithms, and vice versa?**

KNN is appropriate for datasets of small to medium size with few features. It is the better choice when the boundary between classes is not well-defined. However, in the case of large datasets or problems with high dimensions, more efficient algorithms such as Random Forest or Gradient Boosting may be favored.