In the context of binary classification, which metric calculates the ratio of true positives to the sum of true positives and false negatives?
- Precision-Recall Curve
- F1 Score
- True Positive Rate (Sensitivity)
- Specificity
The True Positive Rate, also known as Sensitivity or Recall, calculates the ratio of true positives to the sum of true positives and false negatives. It measures the model's ability to correctly identify positive cases. It is an important metric in binary classification evaluation.
Which method for handling missing data involves using algorithms like k-NN to find similar records to impute the missing value?
- Mean imputation
- Median imputation
- k-NN imputation
- Mode imputation
k-NN imputation is a technique that uses the similarity of data points to impute missing values. It finds records with similar characteristics to the one with missing data and replaces the missing value with the imputed value from its nearest neighbors. Other options are simpler imputation methods.
In recurrent neural networks (RNNs), which variant is designed specifically to handle long-term dependencies by maintaining a cell state?
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- SRU (Simple Recurrent Unit)
- ESN (Echo State Network)
Long Short-Term Memory (LSTM) is a variant of RNN designed to handle long-term dependencies by maintaining a cell state that can capture information over long sequences. LSTM's ability to store and retrieve information over extended time steps makes it well-suited for tasks involving long-term dependencies in data sequences.
Which metric provides a single score that balances the trade-off between precision and recall?
- F1 Score
- Accuracy
- ROC AUC
- Log Loss
The F1 Score is a metric that balances the trade-off between precision and recall. It is especially useful when dealing with imbalanced datasets or when you want to find a balance between correctly identifying positive cases (precision) and capturing all positive cases (recall). The F1 Score is the harmonic mean of precision and recall. It is a suitable choice for evaluating models when both precision and recall are essential.
An AI startup with limited computational resources is building an image classifier. They don't have the capability to train a deep neural network from scratch. What approach can they use to leverage the capabilities of deep learning without the extensive training time?
- Transfer learning
- Reinforcement learning
- Genetic algorithms
- Random forest classifier
Transfer learning allows the startup to use pre-trained deep neural networks (e.g., a pre-trained CNN) as a starting point. This approach significantly reduces training time and computational resources, while still benefiting from the capabilities of deep learning.
A common architecture for real-time data processing involves using ________ to ingest and process streaming data.
- Hadoop
- Spark
- Batch Processing
- Data Lakes
In real-time data processing, Apache Spark is commonly used to ingest and process streaming data. Spark provides the capabilities to handle streaming data in real time, making it a popular choice for such applications.
In a skewed distribution, which measure of central tendency is most resistant to the effects of outliers?
- Mean
- Median
- Mode
- Geometric Mean
The median is the most resistant measure of central tendency in a skewed distribution. It is less affected by extreme values or outliers since it represents the middle value when the data is arranged in order. The mean, mode, and geometric mean can be heavily influenced by outliers, causing them to be less representative of the data's central location.
What is a common technique to prevent overfitting in linear regression models?
- Increasing the model complexity
- Reducing the number of features
- Regularization
- Using a smaller training dataset
Regularization is a common technique used to prevent overfitting in linear regression models. It adds a penalty term to the linear regression's cost function to discourage overly complex models. Regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.
What is the primary purpose of transfer learning in the context of deep learning for computer vision?
- Training a model from scratch
- Fine-tuning a pre-trained model
- Reducing the number of layers in a neural network
- Converting images into text
Transfer learning in computer vision involves fine-tuning a pre-trained model to adapt it for a new task. It leverages knowledge from a source task to improve performance on a target task, making it more efficient and effective than training from scratch.
When evaluating models for a multi-class classification problem, which method computes the average metric score for each class, considering the other classes as the negative class?
- Micro-averaging
- Macro-averaging
- Weighted averaging
- Mini-batch averaging
Macro-averaging computes the average metric score for each class, treating all other classes as the "negative" class. It provides an equal weight to each class and is useful when you want to assess the model's overall performance while giving equal importance to each class, regardless of class size. Macro-averaging can be particularly useful in imbalanced multi-class classification problems.