SVM is sensitive to the choice of kernel, which can significantly affect its performance and requires careful tuning.

Using too many features can lead to higher computational costs and may increase the risk of overfitting.

Random Forests reduce the risk of overfitting by averaging the predictions of multiple decision trees, leading to better generalization.

Random Forests can handle missing values effectively by using multiple trees to fill in gaps.

Support Vector Machines can effectively handle non-linear data by using kernel functions to transform the input space.

SVM is particularly effective in high-dimensional spaces, making it suitable for various applications, including text classification.

SVMs can effectively handle non-linear boundaries through the use of kernel functions.

Latency requirements are crucial for real-time predictions, as the model must respond quickly to user requests.

SVMs utilize kernel functions to transform data into higher dimensions, allowing them to handle non-linear relationships.

A key step in K-means is assigning data points to the nearest cluster centroid based on distance.

Hierarchical clustering can be computationally expensive and is generally limited to smaller datasets due to its complexity.

K-Means is sensitive to outliers, which can skew the results and affect cluster centroids.

Linear regression assumes a linear relationship between the independent and dependent variables, which may not hold true in all cases.

RNNs struggle with long-range dependencies due to issues like the vanishing gradient problem.

A key limitation of K-means is that it requires the number of clusters to be specified beforehand, which can be challenging in practice.

Min-Max scaling is a method used to scale features to a specific range, typically [0, 1].

Recursive Feature Elimination is a method used to select features by recursively removing the least important ones.

Imputation is a technique used to fill in missing values in a dataset.

A dendrogram is a tree-like diagram that shows the arrangement of the clusters formed in hierarchical clustering.

All of the listed issues can affect the performance and validity of a linear regression model.

All of the listed options can be potential problems when using linear regression, affecting the model's performance and interpretability.

Market segmentation is a common application of clustering, where customers are grouped based on purchasing behavior.

Feature engineering is applicable in various real-world scenarios, including image recognition, natural language processing, and fraud detection.

Linear regression is commonly used in stock price prediction to model the relationship between stock prices and various influencing factors.

Neural networks can analyze medical images and patient data to assist in diagnosing diseases.

Random Forests can predict crop yields by analyzing various environmental factors affecting agriculture.

Linear Discriminant Analysis (LDA) is a technique used for dimensionality reduction while preserving class separability.

Customer segmentation in marketing uses clustering to group customers based on purchasing behavior.

Logistic Regression is a classification algorithm used to predict binary outcomes based on input features.

Logistic Regression is a regression algorithm used for predicting binary outcomes, despite its name suggesting classification.