Overfitting is the problem where a model becomes too specialized in the training data and captures its noise and outliers. This can lead to poor performance on unseen data.

The most suitable approach for leveraging knowledge from a model trained on a larger dataset with a small dataset is "Transfer Learning." It involves adapting the pre-trained model to the new task.

Increasing the value of 'k' in the k-NN algorithm can help eliminate noise. A higher 'k' value smooths the decision boundaries, reducing the impact of individual noisy data points.

Q-learning is a type of reinforcement learning that focuses on finding the best action to take in a given state to maximize cumulative rewards.

L1 regularization is a technique used to reduce both bias and variance in a machine learning model. It does so by adding a penalty term to the model's loss function, which encourages the model to use fewer features, thus reducing complexity and variance. Dropout, Cross-Validation, and Data Augmentation are techniques but are not primarily used for regularization.

The "G" in GRU stands for "Gated." GRU is a type of RNN that uses gating mechanisms to control information flow, making it capable of handling sequences efficiently.

The vanishing gradient problem refers to the issue where gradients in deep RNNs become too small during training, making it challenging to capture long-range dependencies.

High Variance is typically associated with complex models with many parameters. Complex models are more flexible and tend to fit the training data closely, resulting in high variance, which can lead to overfitting.

Time series forecasting aims to predict future trends or patterns based on historical data, which can be applied in various fields like finance or weather.

Decision Trees are prone to "Overfitting," where they become too complex and fit the training data too closely. This can lead to poor generalization to new, unseen data.