Hierarchical clustering creates a tree structure (dendrogram) to represent the data.

DBSCAN can find clusters of arbitrary shape, while K-Means assumes spherical clusters.

K-means is a partitional clustering method that divides data into a fixed number of clusters, while hierarchical clustering builds a tree of clusters without needing to specify the number of clusters in advance.

K-means uses centroids to represent clusters, while K-medoids uses actual data points as the center of clusters, making it more robust to outliers.

K-means uses centroids to represent clusters, while K-medoids uses actual data points as cluster representatives.

Logistic regression is used for binary classification tasks, while linear regression is used for predicting continuous values.

The main difference is that regression predicts continuous values, while classification predicts discrete labels.

The main difference is that regression predicts continuous values, while classification predicts discrete labels.

The main difference is that supervised learning uses labeled data for training, while unsupervised learning works with unlabeled data.

Decision Trees can easily overfit the training data, especially if they are deep and complex.

K-means clustering is sensitive to the initial placement of centroids, which can affect the final clustering outcome.

Decision Trees are prone to high variance, meaning they can overfit the training data and perform poorly on unseen data.

Accuracy can be misleading, especially in imbalanced datasets, as it does not account for the distribution of classes.

Accuracy can be misleading in imbalanced datasets, as it may not reflect the true performance of the model.

Activation functions introduce non-linearity into neural networks, allowing them to learn complex patterns.

PCA aims to reduce the number of features while preserving as much variance as possible in the data.

Dimensionality reduction aims to simplify the model while maintaining its performance.

Feature scaling ensures that all features are on a similar scale, which is important for distance-based algorithms.

The main goal of feature selection is to improve model performance by reducing overfitting and enhancing the model's ability to generalize to unseen data.

The main goal of model selection is to identify the model that generalizes best to unseen data.

Cross-validation helps reduce overfitting and provides a better assessment of model performance.

K-Means requires the user to specify the number of clusters in advance, which can be a limitation.

Accuracy can be misleading in imbalanced datasets, as it may give a false sense of model performance by not reflecting the true predictive power for minority classes.

The main limitation of accuracy is that it does not consider false positives and false negatives, which can be misleading in imbalanced datasets.

Accuracy can be misleading in cases of class imbalance, as it does not differentiate between types of errors.

Feature importance helps identify which features contribute most to the predictions made by the Random Forest model.

Pruning reduces the size of the tree, which helps to prevent overfitting and improves generalization to unseen data.

Pruning is used to reduce the size of the tree and prevent overfitting, leading to better generalization on unseen data.

The forget gate in an LSTM determines what information from the previous cell state should be discarded.

The softmax function normalizes the output of the final layer to a probability distribution over multiple classes in classification tasks.