Random Forest in Machine Learning Applications

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What do filter approaches in feature selection focus on?

Maximizing the variance of data along the first axis

Judging the importance of a feature based on variance and correlation (correct)

Evaluating different subsets of feature combinations against an error function

Combining the training of the model with feature selection

In feature selection, what does a wrapper approach evaluate?

The importance of a feature based on variance and correlation

Different subsets of feature combinations against an error function (correct)

The variance of data along the first axis

The training of the model with selection of relevant features

What does Principal Component Analysis (PCA) aim to maximize?

Variance of the data along the first axis (correct)

Performance of the model

Importance of features based on statistics

Subset of relevant features

Which type of feature selection method combines training of the model with selecting relevant features?

Embedded Signup and view all the answers

What is a key focus of wrapper approaches in feature selection?

Evaluating different subsets of feature combinations against an error function Signup and view all the answers

Which method evaluates the importance of features based on statistical metrics like variance and correlation?

Filter approach Signup and view all the answers

What is the main difference between forecasting and prediction?

Forecasting uses historical data to predict future trends, while prediction focuses on determining future outcomes based on current data. Signup and view all the answers

What is a key feature of Convolutional Neural Networks (CNNs)?

CNNs use filters to detect patterns in spatial data like images. Signup and view all the answers

What is the purpose of feature engineering in the context of forecasting?

To transform raw data into useful input features for the model. Signup and view all the answers

Which statement best describes Recurrent Neural Networks (RNNs)?

RNNs have feedback loops that allow information to persist. Signup and view all the answers

In Artificial Neural Networks, what is the 'Activation threshold/bias' used for?

To control when a neuron in the network will fire and pass information to the next layer. Signup and view all the answers

What is the primary objective of backpropagation in neural networks?

To update weights based on errors calculated during forward propagation. Signup and view all the answers

What is the main difference between recurrent neural networks (RNNs) and feedforward neural networks?

Presence of self-loops in RNNs cause the hidden state to change after each sequential input Signup and view all the answers

In which domain are recurrent neural networks (RNNs) suitable?

Time series forecasting Signup and view all the answers

What is a key application area for convolutional neural networks (CNNs)?

Image processing Signup and view all the answers

Which feature is crucial in feature engineering for forecasting tasks?

Temporal features Signup and view all the answers

What do self-loops in recurrent neural networks (RNNs) contribute to?

Changing the hidden state after each input Signup and view all the answers

How do convolutional neural networks (CNNs) process image data?

By using kernels for feature extraction through convolutions Signup and view all the answers