A confusion matrix is a table that summarizes the performance of a classification model by showing true positives, false positives, true negatives, and false negatives.

A confusion matrix is a table that shows the performance of a classification model by comparing predicted and actual values.

The curse of dimensionality encompasses all these challenges that arise when working with high-dimensional data.

Ensemble learning refers to the technique of combining multiple models, such as decision trees in Random Forests, to improve overall prediction accuracy.

The environment is the external system with which the agent interacts and receives feedback in the form of rewards.

Feature engineering involves creating new input features from existing data to improve model performance.

Feature importance indicates how much each feature contributes to the model's predictions, helping to identify the most influential variables.

The learning rate determines how much to change the model in response to the estimated error each time the model weights are updated.

In SVM, the margin is the distance between the closest data points of different classes, which the algorithm aims to maximize.

Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to unseen data.

Overfitting occurs when a model learns the training data too well, resulting in poor generalization to unseen data.

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, leading to poor performance on unseen data.

Subword tokenization refers to breaking words into smaller meaningful units, which helps in handling out-of-vocabulary words.

Accuracy measures the proportion of true results among the total number of cases examined.

Data drift refers to changes in the data distribution over time, which can affect the model's performance and accuracy.

The discount factor determines how much future rewards are valued compared to immediate rewards, influencing the agent's decision-making.

Exploration involves trying new actions to discover their potential rewards, which is essential for learning in uncertain environments.

Clustering is used in market segmentation to identify customer groups with similar behaviors for targeted marketing.

Clustering is often used in marketing to segment customers into distinct groups based on purchasing behavior or demographics.

Customer segmentation is a common application of clustering, where businesses group customers based on purchasing behavior or demographics.

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

Clustering methods are commonly used to segment customers based on purchasing behavior, allowing businesses to tailor marketing strategies.

CNNs are primarily used for image-related tasks, such as image classification and object detection.

Decision trees are commonly used in credit scoring to assess the risk of lending to individuals based on various features.

Decision Trees are often used to predict patient outcomes based on various health metrics.

K-means clustering is commonly used to segment customers into distinct groups for targeted marketing.

K-means clustering is often used in image segmentation to group similar pixels together.

K-means clustering is commonly used for market segmentation to identify distinct customer groups.

Neural networks are widely used for image classification tasks, where they can identify and categorize images based on their content.

Supervised learning is often used in finance for stock price prediction, where historical data is used to predict future prices.