When should you use LIME, counterfactual explanations, or Shapley values in machine learning? LIME for data visualization, CF for feature selection, Shapley for model evaluation LIME for individual predictions, CF for small changes, Shapley for feature contributions LIME for model accuracy, CF for prediction speed, Shapley for data cleaning LIME for large datasets, CF for model training, Shapley for data preprocessing

LIME for individual predictions, CF for small changes, Shapley for feature contributions

What is the primary use of the sigmoid function in machine learning? Multi-class classification with outputs between 0 and 1 Clustering with outputs between 0 and 1 Binary classification with outputs between 0 and 1 Regression analysis with outputs between -1 and 1

Binary classification with outputs between 0 and 1

What is a limitation of using linear activation functions in neural networks? They cause the vanishing gradient problem They do not handle non-linearity, limiting model complexity They require large datasets to function properly They are computationally expensive

They do not handle non-linearity, limiting model complexity

What is the role of Shapley values in machine learning? To reduce the model's complexity To evaluate the contribution of each feature to the model's prediction To optimize the model's hyperparameters To improve the model's training speed

To evaluate the contribution of each feature to the model's prediction

What is the formula for binary cross-entropy loss in machine learning? -[Y log(1-Yhat) + (1-Y) log(Yhat)] Y log(1-Yhat) + (1-Y) log(Yhat) Y log(Yhat) + (1-Y) log(1-Yhat) -[Y log(Yhat) + (1-Y) log(1-Yhat)]

-[Y log(Yhat) + (1-Y) log(1-Yhat)]

What does 'DL' stand for, and how is it positioned within ML and AI? Digital Learning, a subcategory of AI Data Learning, a subcategory of AI Deep Learning, a subcategory of ML and AI Dynamic Learning, a subcategory of ML

Deep Learning, a subcategory of ML and AI

What factors have contributed to the popularity of deep learning? Complex algorithms, high computational cost, and limited applications Limited data availability, outdated technology, and lack of investment Slow processing speeds, high error rates, and limited scalability Accessibility of big data, better technology, improved algorithms, and investments

Accessibility of big data, better technology, improved algorithms, and investments

What is the role of matrices in the architecture of Recurrent Neural Networks (RNNs)? To initialize the weights of the network To reduce the dimensionality of the input data To store the output of the network To transform input vectors and apply to previous hidden states

To transform input vectors and apply to previous hidden states

Which applications are best suited for Recurrent Neural Networks (RNNs)? Linear regression, logistic regression, and decision trees Image classification, object detection, and video analysis Text generation, speech recognition, music generation, and time series prediction Data clustering, anomaly detection, and feature extraction

Text generation, speech recognition, music generation, and time series prediction

What are the key components of a Convolutional Neural Network (CNN)? Convolutional layers, activation layers, pooling layers, fully connected layers, and loss function Data preprocessing, feature extraction, and model evaluation Input layers, hidden layers, output layers, and activation functions Gradient descent, backpropagation, and weight initialization

Convolutional layers, activation layers, pooling layers, fully connected layers, and loss function

When are forecasts generally more accurate? For individual items rather than groups For groups of items rather than individual items For unpredictable markets For longer time horizons

For groups of items rather than individual items

What are the three forecasting horizons? Hourly, Daily, Yearly Immediate, Near-term, Long-term Daily, Weekly, Monthly Long-range, Mid-range, Short-range

Long-range, Mid-range, Short-range

Typically, how long is medium-range forecasting? Hourly for up to 1 month Yearly for up to 5 years Daily for up to 6 months Weekly/monthly for up to 2 years

Weekly/monthly for up to 2 years

Typically, how long is short-range forecasting? Monthly for up to 2 years Hourly/daily for up to several months Yearly for up to 5 years Weekly for up to 1 year

Hourly/daily for up to several months

Why is forecasting important in business planning? It eliminates all risks It is a starting point for business planning It is only used for marketing It guarantees profit

It is a starting point for business planning

What does latent space capture in data? The underlying structure and essential features The irrelevant details The noise and outliers The raw data points

The underlying structure and essential features

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