Estimating the price of a house is an example of a regression application, as it involves predicting a continuous value.

Predicting house prices based on features is a regression problem, as it involves predicting a continuous value.

Predicting the temperature for tomorrow is a regression task as it involves predicting a continuous value.

K-Means Clustering is an unsupervised learning technique that identifies patterns in data without labeled outcomes.

Customer segmentation is an example of unsupervised learning, where the model identifies patterns in data without labeled outcomes.

Clustering similar data points is an example of unsupervised learning, where the model identifies patterns without labeled outcomes.

Market basket analysis is an example of unsupervised learning, where patterns are discovered without labeled outcomes.

Effective feature engineering does not eliminate the need for data preprocessing; it often requires it to prepare the data for modeling.

Increased data redundancy is not a benefit of feature engineering; rather, effective feature engineering aims to reduce redundancy and improve the quality of the features used.

Model training time is not a challenge in deployment; rather, it is a concern during the model development phase.

High upfront costs are not a characteristic of cloud ML services, which typically operate on a pay-as-you-go model.

Hierarchical clustering does not require the number of clusters to be specified in advance; it builds a hierarchy of clusters.

K-means clustering struggles with non-spherical clusters, as it assumes clusters are spherical and equally sized.

Linear regression can handle multiple predictors, not just two variables.

While Random Forests are robust, they do not always guarantee the best accuracy for every dataset.

RNNs are not always faster than feedforward networks; their sequential nature can lead to longer training times.

Supervised learning requires labeled data and does not automatically discover patterns without supervision.

SVM can be adapted for multi-class classification using strategies like one-vs-one or one-vs-all, so it is not limited to binary classification.

Predictive modeling is not a common application of clustering methods, as it typically involves supervised learning rather than unsupervised clustering.

Image classification typically involves supervised learning, while clustering is unsupervised.

Training new models is not an application of deployed models; rather, it is part of the model development process.

SVM is primarily used for classification tasks, while clustering is typically handled by different algorithms.

Hyperparameter tuning is typically a part of the model training process, not a challenge faced during model deployment.

Mean squared error is typically used in regression tasks, while the other options are used for classification tasks.

Random deployment is not a common deployment strategy; the others (Blue-Green, Canary, and Rolling) are widely used.

Logistic distance is not a standard distance metric used in clustering; common metrics include Euclidean, Manhattan, and Cosine similarity.

Mean Squared Error (MSE) is primarily used for regression models, while Precision, Recall, and F1 Score are metrics used for evaluating classification models.

Training loss is not used for evaluating deployed models; it is a measure of performance during training, not in production.

Hierarchical initialization is not a common method for initializing K-means; the other three are standard techniques.

Logistic kernel is not a standard kernel used in SVM; common kernels include linear, polynomial, and RBF.