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.
In the context of machine learning, what is the main difference between supervised and unsupervised learning in terms of data?
- Feature selection
- Hyperparameter tuning
- Labeled data
- Unlabeled data
The main difference between supervised and unsupervised learning is the presence of labeled data in supervised learning. In supervised learning, the model is trained using labeled data, which means it knows the correct answers. Unsupervised learning, on the other hand, works with unlabeled data, where the model has to find patterns and relationships on its own. Feature selection and hyperparameter tuning are aspects of model training but not the key distinction.
Which component of the Actor-Critic model is responsible for evaluating the actions taken by the agent?
- The Critic
- The Actor
- The Decision Maker
- The Environment
The Critic in the Actor-Critic architecture evaluates the actions taken by the agent by providing feedback on the quality of these actions.
An advanced application of NLP in healthcare is the creation of virtual health assistants or ________.
- Chatbots
- Recipe Generators
- Weather Predictors
- Gaming Characters
NLP in healthcare can create virtual health assistants, known as chatbots, to assist with medical inquiries and provide information to patients.
Consider a self-driving car learning from trial and error in a simulated environment. This is an example of which type of learning?
- Deep Learning
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
This scenario exemplifies Reinforcement Learning. In Reinforcement Learning, an agent learns to take actions in an environment to maximize a reward signal. The self-driving car explores different actions (e.g., steering, accelerating, braking) and learns from the consequences in a simulated environment to improve its driving skills.
________ is the problem when a model learns the training data too well, including its noise and outliers.
- Bias
- Overfitting
- Underfitting
- Variance
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.