Introduction to Machine Learning Classification

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Questions and Answers

Which of the following is the primary goal of a supervised learning classification task?

  • Mapping an input point to a discrete category. (correct)
  • Reducing the dimensionality of the input data.
  • Mapping an input point to a continuous value.
  • Discovering hidden patterns in unlabeled data.

In the context of classification, what distinguishes a binary classification problem from a multi-class classification problem?

  • Binary classification uses more complex algorithms than multi-class classification.
  • Binary classification involves continuous target variables, while multi-class involves discrete variables.
  • Binary classification handles only numerical data, while multi-class can handle both numerical and categorical data.
  • Binary classification involves only two classes, while multi-class classification involves more than two classes. (correct)

Why is it important to address imbalanced data in classification problems, and what is a common characteristic of such datasets?

  • Addressing imbalanced data is irrelevant in classification; the characteristic is a large dataset size.
  • Imbalanced data can lead to biased models; the characteristic is unequal distribution of examples across classes. (correct)
  • Imbalanced data improves the accuracy of all classification algorithms; the characteristic is equal distribution of data.
  • Imbalanced data only affects regression problems; the characteristic is high dimensionality.

Which of the following algorithms can be used for classification tasks?

<p>K-Nearest Neighbors, Logistic Regression, and Decision Trees (D)</p>
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What is the key assumption behind the K-Nearest Neighbors (KNN) algorithm for classification?

<p>Data points of the same class tend to cluster together in the feature space. (C)</p>
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In the context of the K-Nearest Neighbors (KNN) algorithm, what impact does increasing the value of 'K' have on the decision boundary?

<p>It makes the decision boundary smoother and less sensitive to noise. (B)</p>
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What is the impact of a high computational cost in K-Nearest Neighbors (KNN)?

<p>It limits scalability for large datasets. (B)</p>
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In a confusion matrix, what does a 'False Positive' indicate?

<p>The model incorrectly predicted the positive class when it was actually negative. (D)</p>
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Why is it necessary to consider metrics beyond just 'Accuracy' when evaluating classification models?

<p>Accuracy can be misleading with imbalanced datasets. (C)</p>
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When evaluating a classification model, what does 'Precision' measure?

<p>The fraction of true positive predictions out of all positive predictions. (C)</p>
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What does 'Recall' measure in the evaluation of a classification model?

<p>The proportion of actual positives that are correctly identified. (B)</p>
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When might the F1-score be a more appropriate evaluation metric than accuracy?

<p>When the dataset is imbalanced or when balanced precision and recall are desired. (B)</p>
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In Support Vector Machines (SVM), what is the primary goal in creating a hyperplane?

<p>To separate the data into classes with the widest possible margin. (C)</p>
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What are 'support vectors' in the context of Support Vector Machines (SVM)?

<p>Data points closest to the decision boundary that influence its position. (D)</p>
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What does maximizing the 'margin' achieve in Support Vector Machines (SVM)?

<p>It helps to improve the generalization performance of the model. (D)</p>
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In the context of Support Vector Machines (SVM), which of the following is a disadvantage of using SVM?

<p>SVMs are incapable of properly handling text data, potentially leading to loss of sequential information and poor performance. (C)</p>
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What is the purpose of the 'kernel' in Support Vector Machines (SVM)?

<p>To transform the input data into a higher-dimensional space where it can be linearly separated. (C)</p>
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What type of classification problem is best addressed using Logistic Regression?

<p>Classification problems with discrete target variables. (A)</p>
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In the context of logistic regression, why is the output of the linear equation passed through a sigmoid function?

<p>To ensure the output values are between 0 and 1, representing probabilities. (B)</p>
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Which of the following best describes the output of a logistic regression model?

<p>A probability score indicating the likelihood of belonging to a particular class. (C)</p>
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How does the decision boundary in logistic regression change when you move from a single feature to multiple features?

<p>It becomes a hyperplane in a higher-dimensional space. (C)</p>
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What is the role of a threshold in logistic regression?

<p>To convert the predicted probabilities into class labels. (B)</p>
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How can logistic regression be used to model non-linear decision boundaries when dealing with more than one feature?

<p>By transforming features (e.g., polynomial features) to create non-linear combinations. (A)</p>
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What is the primary purpose of classification in machine learning?

<p>To assign data points to predefined categories or classes. (B)</p>
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Which of the following scenarios exemplifies a classification problem?

<p>Identifying different species of plants based on their leaf characteristics. (B)</p>
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Consider a dataset where 95% of the observations belong to one class and 5% belong to another. If a classifier predicts every observation belongs to the majority class, what would be its accuracy, and why might this be misleading?

<p>95%, misleading because it gives a false sense of good performance. (A)</p>
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What would be the likely consequence of using a very small value of K in a KNN classification algorithm?

<p>Overfitting, due to sensitivity to local noise. (D)</p>
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Consider a scenario where a classification model predicts whether or not a customer will default on a loan. Which evaluation metric would be most appropriate if the goal is to minimize false negatives (incorrectly predicting that a customer will not default when they actually will)?

<p>Recall (A)</p>
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Two machine learning engineers are training classification models. Engineer A focuses on maximizing precision, while Engineer B prioritizes maximizing recall. In what scenario would maximizing precision be more critical than maximizing recall?

<p>Detecting fraudulent transactions, where false positives could lead to unnecessary investigations. (A)</p>
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In what situations might a linear kernel be preferred over a non-linear kernel (e.g., RBF) in Support Vector Machines (SVM) despite the possibility of non-linear relationships in the data?

<p>When interpretability is important. (D)</p>
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Why is logistic regression more appropriate than linear regression in classification problems?

<p>Linear regression outputs predictions that are not constrained between 0 and 1, resulting in outputs that cannot be interpreted as probabilities. (B)</p>
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A real estate company wants to predict whether a lead will convert into a sale. They have data on various factors, including property prices and the number of bedrooms and bathrooms. Which of the following classification algorithms aligns best with the real estate company's implicit goal of finding a way to calculate the probability of lead conversion?

<p>Logistic Regression (D)</p>
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Consider that a given point has the following feature, $x=-5$. The logistic regression model for classification is defined by $f_{w,b}(x) = g(wx + b)$, where $g(z)$ is the sigmoid function. Assume parameters $w=1$ and $b=0$. Determine the result of the classification.

<p>Likely to belong to class 0 (A)</p>
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A radiologist trains a machine learning classifier to predict instances of cancer using a variety of imaging techniques. The model generates too many instances of benign results being labelled as malignant. In terms of the false positive and false negative rate, explain which is more problematic in medical diagnoses and why?

<p>A high false negative rate (C)</p>
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How does the choice of kernel affect the decision boundary in SVM?

<p>It determines how the model transforms the input space to separate (A)</p>
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What distinguishes supervised learning from unsupervised learning?

<p>Supervised learning requires labeled data for training, whereas unsupervised learning does not. (A)</p>
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Identify the approach most suitable for predicting if an email is spam or not spam.

<p>Supervised Classification (B)</p>
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What is the role of a classification algorithm's parameters?

<p>To adjust how the algorithm learns from the training data. (D)</p>
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What is the purpose of data normalization or standardization before training a classification model?

<p>To ensure that all features contribute equally to the model's learning process. (D)</p>
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Flashcards

What is classification?

A supervised learning task where the goal is to map an input to a discrete category.

What is Binary Classification?

Predicting between two mutually exclusive outcomes.

What is Multi-Class Classification?

Categorizing inputs into more than two distinct classes.

What is Imbalanced Classification?

Classification with a significant disparity in the number of examples for each class.

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What are Classification Algorithms?

Algorithms that assign instances to predefined categories based on learning from labeled data.

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What is K-Nearest Neighbors (KNN)?

An algorithm that classifies data points based on the classes of their nearest neighbors.

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What is Accuracy?

A metric indicating the fraction of correctly classified cases.

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What is Precision?

A metric indicating how precise your model is; when it predicts, how often is it correct.

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What is Recall?

A metric indicating the fraction of actual positives that were correctly identified.

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What is the F1-Score?

A weighted average of precision and recall, used to balance the two metrics.

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What is Support Vector Machine (SVM)?

Representing data as points in space and separating them into categories with a clear gap.

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What are Support Vectors?

Important data points closest to both classes, used to define the boundary.

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What is Margin in SVM?

Distance between support vectors and the dividing line in SVM.

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Maximum Margin Separator

Seeks to maximize the distance between classes in SVM.

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What is a Kernel Function?

Transforming data into a suitable format for machine learning algorithms.

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What is the C-value?

A parameter denoting the penalty for misclassification in SVM.

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What is the Gamma value?

Used in SVM, it properly fits the model to the data.

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What is Logistic Regression?

A machine learning method that predicts the probability of a categorical outcome.

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What is Binary Classification?

A type of classification problem where the outcome has only two possible values.

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What is the Sigmoid Function?

A function used in logistic regression to map predictions to probabilities between 0 and 1.

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What is a Decision Boundary?

Boundary where the predicted probability switches from one class to another.

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Study Notes

  • Introduction to Machine Learning focuses on learning from examples for classification.

Classification

  • It is a supervised learning task where you learn a function mapping an input to a discrete category.
  • A classification problem involves predicting a specific class or category for a given input.
  • Examples include predicting if an animal is a dog, cat, tree, or other animal.

Classification Types

  • Binary classification involves two classes like spam/not spam or male/female.
  • Multi-class classification involves more than two classes, such as music genres or plant diseases.
  • Imbalanced classification deals with unequal distributions of examples in each class, for example, spam or fraud detection.

Classification Algorithms

  • K-Nearest Neighbors (KNN)
  • Logistic Regression
  • Decision Trees
  • Support Vector Machines (SVM)
  • Naive Bayes

Classification Example

  • Determining if Breast Cancer is Malignant or Benign

Rain Prediction Example

  • Machine learning classification is used to predict rain based on humidity and pressure
  • Based on January 1 with 93% and 999.7 pressure it rained
  • Based on January 2 with 49% and 1015.5 pressure it did not rain
  • Based on January 3 with 79% and 1031.1 pressure it did not rain

Nearest-Neighbor Classification

  • It is an algorithm that, given an input, chooses the class of the nearest data point to that input

K-Nearest Neighbors (KNN) Algorithm

  • Assumes data exists in close proximity, classifying new cases based on a similarity measure, like distance functions.
  • Classifies an object by a majority vote of its neighbors in the input parameter space.
  • Assigns the object to the most common class among its k-nearest neighbors, where k is specified by a human.
  • The boundary becomes smoother with increasing values of K; increasing K to infinity results in a uniform classification.

Steps for KNN Algorithm

  • Load the data, initialize K, and for each data point, calculate the distance to the query example
  • Add the distance and index to an ordered collection
  • Sort the collection, pick the first K entries, get the labels, and return the mode of the K labels.

Advantages of KNN

  • It can be used for both classification and regression problems
  • Implementation is easy

Disadvantages of KNN

  • Determining the best k value can be time-consuming for very large data, and it has high computational cost because of computing distance
  • High computational cost

Classification Evaluation Metrics

Confusion Matrix

  • Illustrates True Positive (TP), False Negative (FN), False Positive (FP), and True Negative (TN) values.

Accuracy

  • Accuracy means fraction of cases correctly classified
  • Formula: Accuracy = (TP + TN) / (TP + FP + TN + FN)

Precision

  • Precision measures how accurate the model is, or how often it is correct when it makes a prediction
  • Formula: Precision = TP / (TP + FP)

Recall

  • Recall measures the fraction of cases of a label value correctly classified out of all cases that actually have that label
  • Formula: Recall = TruePositives / FalseNegatives

F1 Score

  • The F1 score is the weighted average of precision and recall
  • Formula: F1 = 2 * (precision * recall) / (precision + recall)

Support Vector Machines (SVM)

  • Represents training data as points in n-dimensional space, separated into categories by a clear gap.
  • The basic principle is to create a hyperplane that separates the dataset into classes.
  • Support vectors are the points closest to both classes.
  • Steps include finding the proximity between the dividing plane and support vectors.
  • Margin is the distance between points and the dividing line.
  • Aims to maximize the margin; a hyperplane becomes optimal when the margin reaches its maximum.

Support Vector Machines (SVM)

  • H1 does not separate the classes.
  • H2 separates the classes but has a small margin.
  • H3 separates the classes with the maximum margin.

SVM Parameters

  • Kernel transforms输入data into required format
  • C-value: This is a penaltyparameter denoting misclassification
  • Gamma value: This fits themodel properly

SVM Advantages

  • Effective in high dimensional spaces and memory efficient, using a subset of points in the decision function.

SVM Disadvantages

  • Incapable of handling text data properly loss of sequential information leads to poor performance

Logistic Regression

  • Used for binary classification where the output "y" can only be one of two values.

Logistic Regression Model - "y" values

  • The answer to questions like "Is this email spam?" or "Is the tumor malignant?"

Sigmoid (Logistic) Function

  • Used to produce outputs between 0 and 1, representing probabilities.
  • Formula: g(z) = 1 / (1 + e^-z), where 0 < g(z) < 1
  • The "logistic regression" model computes f w,b(x) = g(w•x + b), where g is the sigmoid function.

Logistic Regression Model - "y" values

  • Outputs the probability that the class is 1
  • f w,b(x) represents P(y = 1; w,b), meaning the probability that y is 1, given input x and parameters w,b.
  • It gives the “probability" that class is 1.

Logistic Regression Decision Boundary

  • For more than one feature, the decision boundary is defined by z = w•x + b = 0. Also, it is equal to f w,b(x) = g(W1x1+W2x2 + b).

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