Machine Learning Decision Trees Quiz
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What is the primary purpose of the Attribute Selection Measure (ASM) in decision trees?

  • To select the best splitting criterion for partitioning data. (correct)
  • To minimize the number of features in the dataset.
  • To ensure the model prevents any overfitting.
  • To determine the target feature value of unseen instances.

    • Which step should NOT be performed when building a decision tree?

  • Select a test for the root node.
  • Create branches for each outcome of the test.
  • Stop recursion when all instances in a branch have the same class.
  • Combine subsets to increase the number of instances. (correct)

    • In estimating class probabilities, how does a decision tree determine the likelihood that an instance belongs to class k?

  • By adding the probabilities of each feature considered in the branch.
  • By averaging the probabilities from all leaf nodes.
  • By calculating the overall mean of class probabilities.
  • By looking at the proportion of training instances in the corresponding leaf node. (correct)

    • What happens when a branch in a decision tree reaches a point where all instances belong to the same class?

    <p>The branch is considered a terminal node.</p> Signup and view all the answers

    Which step is involved in the process of building a decision tree?

    <p>Recursively applying the splitting process to create branches.</p> Signup and view all the answers

    What does it mean for a computer program to learn from experience in the context of machine learning?

    <p>It adapts its performance at tasks based on previous experiences.</p> Signup and view all the answers

    Which type of machine learning involves predicting a continuous outcome from input data?

    <p>Regression</p> Signup and view all the answers

    In the context of a confusion matrix, what does a true positive (TP) represent?

    <p>Positive examples that are correctly classified as positive.</p> Signup and view all the answers

    Which term describes the percentage of actual positive instances that are correctly identified?

    <p>True positive rate</p> Signup and view all the answers

    What is the goal of unsupervised learning in machine learning?

    <p>To categorize data into groups without pre-labeled examples.</p> Signup and view all the answers

    Which of the following is NOT a characteristic of reinforcement learning?

    <p>It requires labeled training data to learn effectively.</p> Signup and view all the answers

    What is the primary purpose of training and test sets in machine learning?

    <p>To ensure that algorithms can classify future input correctly.</p> Signup and view all the answers

    What is the correct count of images that contain cats based on the model's predictions?

    <p>160</p> Signup and view all the answers

    Which of the following statements best describes the essence of machine learning?

    <p>Machine learning seeks to improve performance through data-based experience.</p> Signup and view all the answers

    Which type of learning algorithm is used to predict outcomes based on labeled data?

    <p>Supervised learning</p> Signup and view all the answers

    In the context of decision trees, what is the main goal when splitting the dataset?

    <p>To achieve pure sub-datasets regarding the target feature</p> Signup and view all the answers

    What is indicated by the term 'leaf nodes' in decision tree algorithms?

    <p>The final predictions made for new instances</p> Signup and view all the answers

    How many total images were used to test the model's performance?

    <p>200</p> Signup and view all the answers

    Which type of learning aims at learning from both labeled and unlabeled data?

    <p>Semi-supervised learning</p> Signup and view all the answers

    What type of problems can decision trees be used for?

    <p>Both classification and regression tasks</p> Signup and view all the answers

    What is the purpose of the stopping criterion in decision trees?

    <p>To indicate when to stop splitting the dataset</p> Signup and view all the answers

    What is the primary goal of minimizing the within-cluster sum of squares (WCSS)?

    <p>To achieve the smallest possible WCSS</p> Signup and view all the answers

    What assumption is implicit in minimizing WCSS?

    <p>SSE is similar for each group</p> Signup and view all the answers

    What is the primary goal of the k-means clustering algorithm?

    <p>To partition observations into specified clusters based on proximity to cluster means</p> Signup and view all the answers

    Which process is generally used to model decision-making in reinforcement learning?

    <p>Markov Decision Process (MDP)</p> Signup and view all the answers

    What is a significant weakness of the k-means clustering algorithm?

    <p>It can be challenging to determine the appropriate number of clusters, K</p> Signup and view all the answers

    In reinforcement learning, what is the role of the agent?

    <p>To explore the environment and make decisions</p> Signup and view all the answers

    What distinguishes reinforcement learning from supervised learning?

    <p>Reinforcement learning does not provide explicit guidance for actions.</p> Signup and view all the answers

    During which step of the k-means algorithm do cluster centroids get recalculated?

    <p>After assigning objects to the closest cluster</p> Signup and view all the answers

    What type of distance measure is primarily used in the k-means algorithm?

    <p>Squared Euclidean distance</p> Signup and view all the answers

    What does Q-learning primarily seek to determine?

    <p>The optimal policy to maximize long-term rewards</p> Signup and view all the answers

    What initial action does the Q-learning algorithm perform?

    <p>Randomly choose an initial state</p> Signup and view all the answers

    What could be a consequence of using the k-means algorithm without rescaling data?

    <p>Clusters may be misrepresented due to differing scales in dimensions</p> Signup and view all the answers

    What is the initial purpose of selecting K in the k-means algorithm?

    <p>To determine the number of potential clusters to form</p> Signup and view all the answers

    What is indicated by the variable R in the context of reinforcement learning?

    <p>The reward received after executing an action</p> Signup and view all the answers

    At what point does the k-means algorithm determine that it has converged?

    <p>When there are no changes in cluster assignments</p> Signup and view all the answers

    Which characteristic of k-means clustering could hinder its effectiveness with categorical data?

    <p>Its reliance on a distance measure that cannot be applied effectively to nominal data</p> Signup and view all the answers

    Which statement is true about decision tree learning models like ID3 and C4.5?

    <p>They are generally fast to train and easy to interpret.</p> Signup and view all the answers

    What assumption underlies the Naïve Bayes classifier?

    <p>Attributes are independent of each other.</p> Signup and view all the answers

    What is the primary purpose of clustering in unsupervised algorithms?

    <p>To group similar items together.</p> Signup and view all the answers

    Which of the following is a method for evaluating clustering effectiveness?

    <p>Manual inspection and distance measures.</p> Signup and view all the answers

    Which of the following statements is false regarding Naïve Bayes classifiers?

    <p>They are guaranteed to provide the highest accuracy.</p> Signup and view all the answers

    How is the distance between two items calculated in clustering with multiple numeric attributes?

    <p>Using the Euclidean distance formula.</p> Signup and view all the answers

    Which characteristic should clusters have in an effective clustering approach?

    <p>High similarity within clusters and low similarity across clusters.</p> Signup and view all the answers

    What is a limitation of decision tree models in classification tasks?

    <p>Their accuracy is often not state-of-the-art.</p> Signup and view all the answers

    Study Notes

    Introduction to Machine Learning

    • Advanced Topics in Computer Systems and Networks
    • Introduction to Machine Learning covered in course

    Contents

    • Definition of machine learning
    • Basic concepts and terms
    • Types of machine learning algorithms
      • Supervised learning
        • Classification
        • Regression
      • Unsupervised learning
        • Clustering
      • Reinforcement learning

    Definition of Machine Learning

    • Machine learning is the study that gives computers the ability to learn without explicit programming
    • It aims to make computers more similar to humans in their ability to learn

    Machine Learning Algorithms (1)

    • Machine learning is the study of learning algorithms
    • A computer program learns from experience E with respect to a class of tasks T and performance measure P if its performance at tasks in T improves with experience E
    • This involves data, learning algorithm(task) and understanding performance

    Machine Learning Algorithms (2)

    • Learning involves experience, induction of a law, prediction of future, and finally using a model for predictions on new data to find unknown attributes
    • This includes historical data, training and prediction

    Basic Concepts and Terms (1)

    • Dataset: collection of data
    • Training set: data used for training a machine learning model
    • Test set: data used to evaluate a machine learning model

    Basic Concepts and Terms (2)

    • Terms
      • P: positive examples
      • N: negative examples
      • TP: true positive
      • TN: true negative
      • FP: false positive
      • FN: false negative
      • Confusion matrix: table to evaluate model accuracy

    Basic Concepts and Terms (3)

    • Measure and Formula
      • Accuracy: (TP + TN) / (P + N)
      • Error rate: (FP + FN) / (P + N)
      • Sensitivity/Recall: TP / P
      • Specificity: TN / N
      • Precision: TP / (TP + FP)
      • F-score: harmonic mean of precision and recall
      • Fβ (β is non-negative real number)

    Basic Concepts and Terms: Example

    • Trained model to identify cats in images
    • Precision: TP / (TP + FP) = 87.5%
    • Recall: TP / P = 93.3%
    • Accuracy: (TP + TN) / (P + N) = 85%

    Types of Machine Learning Algorithms (1)

    • Supervised learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning

    Types of Machine Learning Algorithms (2)

    • Machine learning algorithms can be classified as supervised, unsupervised, and reinforcement learning methods
    • Supervised learning: classification, regression
    • Unsupervised learning: clustering, association analysis, dimensionality reduction
    • Reinforcement learning: model-free, model-based

    Types of Machine Learning Algorithms (2)

    • Shows subcategories of machine learning algorithms: supervised, unsupervised and reinforcement
    • Show details about the algorithms like K-means, PCA, SVD, LDA, DBSCAN etc

    Supervised Learning Algorithms

    • Classification: (Logistic Regression, Naive Bayes, K-Nearest Neighbor, Support Vector Machine)
    • Regression: (Linear Regression, Ridge Regression, Ordinary Least Squares, Stepwise Regression)

    Classification: Decision Trees (1)

    • Decision trees use Gini Index and Entropy to decide criteria
    • Gini Index
      • Measures the probability of misclassifying a randomly chosen element from the set
    • Entropy measures the impurity of the data

    Classification: Decision Trees (2)

    • Decision trees find the best feature with descriptive features that split data, improving purity of groups
    • Leaf nodes store predictions for new data

    Classification: Building a Decision Tree Model (1)

    • Shows visual representation of a Decision Tree model

    Classification: Building a Decision Tree Model (2)

    • Shows flow diagram of data processing in a decision tree

    Classification: Building a Decision Tree Model (3)

    • Decision tree model process is given with example

    Classification: Decision Trees Example (1)

    • Example dataset related to weather data and play to illustrate decision tree usage

    Classification: Decision Trees Example (2)

    • Details the calculation of Gini impurity and Entropy

    Classification: Exploiting a Decision Tree Learning Model

    • Estimating Class Probabilities
    • A decision tree finds the leaf node for an instance and returns the ratio of training instances in the node

    Classification: Exploiting a Decision Tree Learning Model (continued)

    • Best algorithms are ID3, C4.5, CART for training and evaluation

    Classification: Naïve Bayes Classifier

    • Bayesian classifiers use Bayes' theorem to classify objects, assuming attributes are independent
    • This creates predictive probabilities

    Classification: Naïve Bayes (Bayes' Theorem)

    • Relationship between P(B|A) and P(A|B) expressed through Bayes' theorem

    Classification: Naïve Bayes (Bayes' Theorem for classification)

    • Given input features, use Bayes' theorem for classification by finding a class that maximizes probability

    Classification: Naïve Bayes (Bayes' Theorem for classification) (cont)

    • Training Naïve Bayes involves estimating probabilities

    Classification: Naïve Bayes Classifier (cont)

    • Fast and reliable for text and attribute-independent problems, also good when attributes >> number of instances

    Unsupervised Algorithms: Clustering

    • Identifying groups of similar items within a dataset
    • Key concepts include intra-cluster similarity, and inter-cluster dissimilarity

    Unsupervised Algorithms: Clustering (cont)

    • Types include K-Means, K-Median, Hierarchical clustering, DBScan, Expectation Maximization

    Unsupervised Algorithms: Clustering Evaluation

    • Cluster quality measures include manual review, benchmarks for existing labels, and distance measures (high within, low across clusters)

    Unsupervised Algorithms: Clustering (Distance functions)

    • Simplest case: one numeric attribute (A) - Distance (X,Y) = A(X) – A(Y)
    • Multiple numeric attributes: - Distance(X,Y) - Euclidean
    • Attributes may have differing importance, and weighting may be necessary

    Unsupervised Algorithms: Clustering (Algorithm K-Means)

    • Partitions observations into 'k' clusters, with each observation belonging to the cluster with the nearest mean
    • Shows diagrammatic visualization

    Unsupervised Algorithms: Clustering (K-Means: Cluster Center)

    • Objective is to minimize the average squared Euclidean distance of values from their cluster centers.
    • Cluster centers (means) represent the mean value for a cluster

    Unsupervised Algorithms: Clustering (K-Means Clustering)

    • Explains the stepwise algorithm of K-Means Clustering

    Unsupervised Algorithms: Clustering (K-means Clustering) (steps)

    • Initializing K cluster seeds
    • Measuring distances to clusters
    • Assigning objects to clusters
    • Calculating new centroid for each cluster
    • Iteration

    Unsupervised Algorithms: Clustering (K-means Issues)

    • Distance measure is squared Euclidean, scale should be similar across dimensions
    • Not suitable for nominal data
    • Approach tries to minimize within-cluster sum of squares error (WCSS)

    WCSS (Within-Cluster Sum of Squares)

    • Overall WCSS is given by Σ(xi−μi)²
    • Finding the smallest WCSS is a goal in K-means

    Reinforcement Learning

    • Deals with sequential decision-making.
    • The goal is to maximize the cumulative reward over time

    Reinforcement Learning Algorithms

    • Types include model-free (Q-learning, hybrid, policy optimization) and model-based

    Reinforcement Algorithms: Q-Learning

    • Input: states, actions, output: final state
    • Algorithm pseudocode is presented
    • Concepts of agents, states, actions, rewards, and episodes

    Reinforcement Algorithms: Q-Learning (cont)

    • Methods for determining Q-values explained (temporal difference and Bellman's equation)

    Reinforcement Algorithms: Q-Learning (Bellman's Equation)

    • The Bellman Equation is given
    • States the equation in terms of Q-values, reward function, discount factor, and maximum Q-value

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    Machine Learning - Blida 1 University

    Description

    Test your knowledge on decision trees in machine learning with this informative quiz. Explore key concepts such as attribute selection, class probabilities, and learning types. Engage with questions about confusion matrices and the various learning methods within machine learning.

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