Exploring Machine Learning Concepts

What is a decision tree in machine learning?

• A decision tree is a type of neural network.

• A decision tree is a linear regression model.

• A decision tree is a model that uses a tree structure to make decisions based on input features.

• A decision tree is a clustering algorithm.

How does a decision tree make predictions?

• A decision tree predicts outcomes by averaging all feature values.

• A decision tree predicts outcomes based on random selection of features.

• A decision tree uses a single feature to make predictions without splits.

• A decision tree predicts outcomes by traversing from the root to a leaf node based on feature splits.

What are the advantages of using decision trees?

• Advantages of using decision trees include interpretability, minimal data preprocessing, ability to handle various data types, and clear visualization of decisions.

• Requires extensive data cleaning

• High computational cost

• Limited to numerical data only

What are the limitations of decision trees?

• Limitations of decision trees include overfitting, sensitivity to noise, poor performance on unbalanced datasets, and difficulty in capturing complex relationships.

• They require a large amount of data to function properly.

• Decision trees can easily handle high-dimensional data without issues.

• Decision trees are always accurate regardless of data quality.

Define artificial neural networks (ANNs).

• Artificial Neural Networks (ANNs) are physical models of human brains.

• ANNs are simple algorithms that do not require data to learn.

• Artificial Neural Networks (ANNs) are computational models that simulate the way human brains process information, consisting of interconnected layers of nodes that learn from data.

• Artificial Neural Networks (ANNs) are only used for image processing tasks.

What is the basic structure of an artificial neural network?

• Input layer only

• The basic structure of an artificial neural network includes an input layer, hidden layers, and an output layer.

• Output layer and feedback loop

• Hidden layers without input layer

How do activation functions work in ANNs?

• Activation functions in ANNs transform the weighted sum of inputs into an output, enabling non-linear decision boundaries.

• Activation functions are solely responsible for weight initialization.

• Activation functions are used to reduce the number of neurons in a layer.

• Activation functions only amplify the input values.

What is the purpose of the backpropagation algorithm?

• To increase the accuracy of predictions without changing weights.

• To minimize the error in neural networks by adjusting weights through gradient descent.

• To simplify the architecture of neural networks.

• To generate random weights for neural networks.

Explain the concept of deep learning.

• Deep learning is a type of machine learning that uses multi-layered neural networks to analyze various forms of data.

• Deep learning is a type of traditional programming that uses simple algorithms.

• Deep learning is a method for data storage and retrieval.

• Deep learning is a form of data visualization that focuses on graphical representation.

How does deep learning differ from traditional machine learning?

• Deep learning is only applicable to structured data, while traditional methods handle unstructured data.

• Deep learning differs from traditional machine learning by using deep neural networks to automatically learn features from data, while traditional methods rely on manual feature extraction and simpler algorithms.

• Traditional machine learning requires deep neural networks for feature extraction.

• Deep learning uses simpler algorithms than traditional machine learning.