If you want to predict whether an email is spam (1) or not spam (0), which regression technique would you use?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
For this classification task (spam or not spam), Logistic Regression is appropriate. It models the probability of the email being spam and maps it to a binary outcome.
The value at which the sigmoid function outputs a 0.5 probability, thereby determining the decision boundary in logistic regression, is known as the ________.
- Decision Point
- Inflection Point
- Sigmoid Threshold
- Threshold Value
The value at which the sigmoid function outputs a 0.5 probability is known as the decision point. This is the threshold value that separates the two classes in a binary logistic regression.
In which learning approach does the model learn to...
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
In reinforcement learning, a model learns by interacting with an environment and receiving rewards or penalties based on its actions. It aims to make decisions to maximize cumulative rewards.
What is the primary reason for using Random Forests over a single Decision Tree in many applications?
- Faster training time
- Increased accuracy
- Lower memory usage
- Simplicity
Random Forests are preferred due to their increased accuracy over single Decision Trees. They work by aggregating the predictions of multiple trees, which reduces overfitting and results in better overall performance.
n the context of CNNs, why are pooling layers important despite them leading to a loss of information?
- Pooling layers help reduce the spatial dimensions, aiding in computation
- Pooling layers introduce non-linearity and increase model complexity
- Pooling layers reduce the number of filters in the network
- Pooling layers improve interpretability of features
Pooling layers are crucial for dimensionality reduction, making computations feasible, and for creating translation-invariant features. Despite information loss, it retains the most essential features.
In the context of machine learning, what is the primary concern of fairness?
- Bias
- Overfitting
- Underfitting
- Feature Selection
The primary concern in fairness within machine learning is 'Bias.' Bias can lead to unequal treatment or discrimination, especially when making predictions in sensitive areas like lending or hiring.
In ________ learning, algorithms are trained on labeled data, where the answer key is provided.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
In supervised learning, algorithms are trained using labeled data, which means each input is associated with the correct answer. This helps the algorithm learn and make predictions or classifications.
Which term refers to the error introduced by the tendency of a model to fit the training data too closely, capturing noise?
- Bias
- Overfitting
- Underfitting
- Variance
Overfitting is the term used to describe when a model fits the training data too closely, capturing noise and leading to poor generalization on unseen data. It results in a high variance.
When a machine learning algorithm tries to group data into clusters without prior labels, it is using which type of learning?
- Reinforcement Learning
- Semi-Supervised Learning
- Supervised Learning
- Unsupervised Learning
Unsupervised Learning is the technique where the algorithm groups data into clusters without prior labels. This can help identify patterns and relationships within the data.
Hierarchical clustering can be broadly classified into two types based on how the hierarchy is constructed. What are these two types?
- Agglomerative and Divisive
- Linear and Non-linear
- QuickSort and MergeSort
- Recursive and Iterative
Hierarchical clustering can be Agglomerative (bottom-up) or Divisive (top-down) based on how clusters are merged or divided in the hierarchy.