How does the architecture of a CNN ensure translational invariance?
- CNNs use weight sharing in convolutional layers, making features invariant to translation
- CNNs utilize pooling layers to reduce feature maps size
- CNNs randomly initialize weights to break translational invariance
- CNNs use a large number of layers for translation invariance
CNNs ensure translational invariance by sharing weights in convolutional layers, allowing learned features to detect patterns regardless of their location in the image. This is a key property of CNNs.
For binary classification tasks, which regression outputs a probability score between 0 and 1?
- Lasso Regression
- Linear Regression
- Logistic Regression
- Support Vector Regression
Logistic Regression outputs probability scores between 0 and 1, making it suitable for binary classification. It uses the logistic function to model the probability of the positive class.
If you want to visualize high-dimensional data in a 2D or 3D space, which of the following techniques would be suitable?
- Principal Component Analysis
- Regression Analysis
- Naive Bayes
- Linear Discriminant Analysis
Principal Component Analysis (PCA) is suitable for visualizing high-dimensional data in a lower-dimensional space. It identifies the directions of maximum variance, making data more manageable for visualization.
When an agent overly focuses on actions that have previously yielded rewards without exploring new possibilities, it might fall into a ________ trap.
- Exploitation
- Exploration
- Learning
- Reward
If an agent overly focuses on actions that have yielded rewards in the past, it falls into an exploitation trap, neglecting the exploration needed to find potentially better actions.
A machine learning model trained for predicting whether an email is spam or not has a very high accuracy of 99%. However, almost all emails (including non-spam) are classified as non-spam by the model. What could be a potential issue with relying solely on accuracy in this case?
- Data Imbalance
- Lack of Feature Engineering
- Overfitting
- Underfitting
The issue here is data imbalance, where the model is heavily biased toward the majority class (non-spam). Relying solely on accuracy in imbalanced datasets can be misleading as it doesn't account for the misclassification of the minority class (spam), which is a significant problem.
When the outcome variable is continuous and has a linear relationship with the predictor variables, you would use ________ regression.
- Linear
- Logistic
- Polynomial
- Ridge
Linear regression is used when there is a continuous outcome variable, and the relationship between the predictor variables and the outcome is linear. It's a fundamental technique in statistics and machine learning for regression tasks.
When a model is trained on one task and the learned features are used as a starting point for a model on a second task, it's known as ________.
- Transfer Learning
- Data Augmentation
- Ensemble Learning
- Gradient Boosting
Transfer learning is a technique where knowledge gained from one task is applied as the starting point for another task. This helps leverage pre-trained models and speeds up learning on the new task.
Which application of machine learning in healthcare helps in predicting patient diseases based on their medical history?
- Diagnostic Prediction
- Medication Recommendation
- Patient Scheduling
- X-ray Image Analysis
Machine learning in healthcare is extensively used for Diagnostic Prediction, where algorithms analyze a patient's medical history to predict diseases.
A fashion company wants to create new designs based on current fashion trends. They decide to use machine learning to generate these designs. Which technology would be best suited for this purpose?
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Support Vector Machines (SVM)
- Decision Trees
Convolutional Neural Networks (CNN) are particularly well-suited for image-related tasks, making them ideal for generating fashion designs based on trends. CNNs can capture intricate patterns and styles within images, which is essential in the fashion industry.
In the context of regularization, what is the primary difference between L1 and L2 regularization?
- L1 regularization adds the absolute values of coefficients as a penalty, leading to feature selection
- L1 regularization adds the squared values of coefficients as a penalty, promoting sparsity
- L2 regularization adds the absolute values of coefficients as a penalty, promoting sparsity
- L2 regularization adds the squared values of coefficients as a penalty, leading to feature selection
L1 regularization, also known as Lasso, adds the absolute values of coefficients as a penalty, which promotes feature selection by driving some coefficients to zero. In contrast, L2 regularization, or Ridge, adds the squared values of coefficients as a penalty, which doesn't drive coefficients to zero and instead promotes a "shrinking" effect.