Machine Learning 600 - Introduction

Decision trees allow for white-box testing, enabling users to observe their internal workings.

They can create overly complex models, which may lead to overfitting the training data.

It uses information gain, calculating the difference in entropy before and after the split.

<p>C4.5 can handle continuous attributes and missing values more effectively.</p> Signup and view all the answers

<p>Overfitting occurs when a decision tree model fits the training data too closely, capturing noise.</p> Signup and view all the answers

<p>Pruning the decision tree can reduce its complexity and help avoid overfitting.</p> Signup and view all the answers

<p>A larger training set provides more data points, allowing the tree to learn more patterns.</p> Signup and view all the answers

<p>Underfitting occurs when a model is too simplistic to capture the underlying patterns in data.</p> Signup and view all the answers

<p>The K-Means algorithm is used to partition data into K distinct clusters by minimizing the variances within each cluster.</p> Signup and view all the answers

<p>Decision trees make predictions by splitting data based on feature values to form branches until a final decision node is reached.</p> Signup and view all the answers

<p>Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying patterns, which harms its performance on new data.</p> Signup and view all the answers

<p>Common strategies include pruning the tree, limiting the maximum depth, and setting a minimum number of samples required to split a node.</p> Signup and view all the answers

<p>The silhouette method measures how similar an object is to its own cluster compared to other clusters, helping to evaluate the quality of clustering.</p> Signup and view all the answers

<p>Decision trees are easy to interpret, require little data preprocessing, and can handle both categorical and numerical data effectively.</p> Signup and view all the answers

<p>Decision trees can be prone to overfitting, are sensitive to small data variations, and may not perform well with imbalanced datasets.</p> Signup and view all the answers

<p>The primary advantage of using decision trees is their ease of interpretation and ability to handle both numerical and categorical data.</p> Signup and view all the answers

<p>The K-means algorithm clusters data by partitioning it into K distinct groups based on feature similarity, using the mean of the data points in each cluster.</p> Signup and view all the answers

<p>Overfitting occurs when a model learns the training data too well, capturing noise rather than the underlying distribution, which can lead decision trees to make poor predictions on unseen data.</p> Signup and view all the answers

<p>Underfitting occurs when a decision tree is too simple to capture the underlying structure of the data, often due to insufficient depth or splits.</p> Signup and view all the answers

<p>To avoid overfitting, one can limit the maximum depth of the tree, require a minimum number of samples to split, or use techniques like pruning.</p> Signup and view all the answers

<p>The choice of K is influenced by the dataset's structure, the desired level of granularity in clustering, and methods like the elbow method for optimization.</p> Signup and view all the answers

<p>Entropy measures the impurity or randomness of a dataset; it is used to determine the best splits at each node in a decision tree.</p> Signup and view all the answers

<p>Yes, decision trees can handle both numerical and categorical data, making them versatile for various types of datasets.</p> Signup and view all the answers