Machine Learning 1 - Week 2 Lecture Quiz
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Questions and Answers

Which of the following is NOT a common data pre-processing technique used in statistical thinking for model validation?

  • Hyperparameter Optimization (correct)
  • Transformation
  • Imputation
  • Dimensionality Reduction

    • What is the primary goal of 'prescriptive' analytics?

  • To identify the reasons behind past trends.
  • To describe past performance and historical patterns.
  • To predict future trends based on historical data.
  • To recommend solutions or actions to influence future outcomes. (correct)

    • In the context of statistical thinking, how does addressing outliers differ from the approach in machine learning?

  • Statistical thinking focuses on understanding the underlying cause of outliers, while machine learning primarily aims to minimize their impact on predictions. (correct)
  • Statistical thinking utilizes outlier removal techniques, while machine learning employs imputation methods.
  • Machine learning treats outliers as errors, while statistical thinking seeks to understand their significance.
  • Both approaches prioritize understanding the root cause of outliers.

    • Which of these is a key difference between feature selection in statistical thinking and machine learning?

    <p>Statistical thinking focuses on understanding the relationships between features and the target variable, while machine learning prioritizes selecting features that maximize predictive power. (C)</p> Signup and view all the answers

    Which analytical category would you use to determine the root cause of customer churn?

    <p>Diagnostic (B)</p> Signup and view all the answers

    Consider the following scenarios: 1. A large retail chain wants to predict customer purchase behavior. 2. A pharmaceutical company wants to identify potential drug candidates based on their molecular structure. Which scenario is more likely to emphasize result interpretation as a primary focus and why?

    <p>Scenario 2, because it involves identifying potential drug candidates, focusing on interpretation. (A)</p> Signup and view all the answers

    According to the Bias-Variance Tradeoff, what happens when you increase the complexity of a model?

    <p>Bias decreases and variance increases. (B)</p> Signup and view all the answers

    What is the main difference between statistical thinking and machine learning thinking?

    <p>Statistical thinking emphasizes quantifying uncertainty, while machine learning prioritizes finding patterns in data. (D)</p> Signup and view all the answers

    Which of the following is NOT a step typically involved in the machine learning workflow?

    <p>Hypothesis Testing (D)</p> Signup and view all the answers

    In a typical machine learning workflow, what is the main purpose of cross-validation?

    <p>To evaluate the model's performance on unseen data. (D)</p> Signup and view all the answers

    Which of the following best describes Occam's Razor, as applied to machine learning?

    <p>Simplicity should be prioritized when designing models. (A)</p> Signup and view all the answers

    What does the concept of 'irreducible error' represent in the Bias-Variance Tradeoff?

    <p>Errors due to inherent randomness or noise in the data. (A)</p> Signup and view all the answers

    Which of the following statements accurately describes the difference between binary classification and multiclass classification?

    <p>Binary classification involves predicting a single label from two possible options, while multiclass classification predicts a label from multiple options. (B)</p> Signup and view all the answers

    What is the primary role of a confusion matrix in evaluating a classification model?

    <p>Assessing the model's ability to correctly classify different classes of data. (C)</p> Signup and view all the answers

    Why is minimizing both bias and variance crucial in machine learning?

    <p>Minimizing bias and variance are both necessary for accurate predictions and for the model to generalize well to new data. (B)</p> Signup and view all the answers

    Why is it crucial to evaluate a classification model using test data that was not used for training?

    <p>To determine the model's ability to generalize to unseen data. (A)</p> Signup and view all the answers

    When might increasing model complexity be justified despite Occam's Razor?

    <p>When the data is highly complex and requires a more sophisticated model. (D)</p> Signup and view all the answers

    Which of the following best describes the concept of 'inference' in the context of statistical thinking?

    <p>The process of drawing conclusions based on data analysis. (D)</p> Signup and view all the answers

    Which of the following is NOT a characteristic of machine learning thinking?

    <p>Developing models that can be easily interpreted by humans. (B)</p> Signup and view all the answers

    Which of the following tasks would most likely be handled by a machine learning algorithm?

    <p>Predicting the price of a stock based on historical data. (D)</p> Signup and view all the answers

    If a model predicts all published papers will not win a Nobel Prize, and the actual number of papers that win a Nobel Prize is very small, which of these metrics will likely be high?

    <p>Accuracy (D)</p> Signup and view all the answers

    In a binary classification problem, what does a high threshold value generally lead to?

    <p>More True Negatives (TN) (C)</p> Signup and view all the answers

    Which of the following correctly defines the F1-Score? It is...

    <p>The harmonic mean of precision and recall. (B)</p> Signup and view all the answers

    In a binary classification model, which of the following is NOT a direct consequence of moving the decision boundary towards the positive class?

    <p>Increasing the number of True Negatives (TN) (A)</p> Signup and view all the answers

    What is the relationship between the decision boundary and the threshold in a binary classification model?

    <p>The decision boundary is set by the threshold, and moving the threshold changes the decision boundary. (D)</p> Signup and view all the answers

    If we increase the threshold in a classifier, what will likely happen to the Precision and Recall metrics?

    <p>Precision will increase, and Recall will decrease. (A)</p> Signup and view all the answers

    In a classification model, what is the primary purpose of the fit() method?

    <p>To learn the relationships between inputs and outputs from the training data. (D)</p> Signup and view all the answers

    Assume a model has a large number of True Negatives (TN) and a small number of True Positives (TP). What can we conclude about the model's bias?

    <p>The model is biased towards predicting negative examples. (C)</p> Signup and view all the answers

    Which of the following metrics is primarily affected by the presence of False Positives (FP)?

    <p>Precision (A)</p> Signup and view all the answers

    What is the main difference between X_train and X_test in a machine learning context?

    <p><code>X_train</code> contains the data used to train the model, and <code>X_test</code> contains unseen data used to evaluate the model's performance. (A)</p> Signup and view all the answers

    Flashcards

    Descriptive Analytics

    Analyzes past performance and trends to answer what happened.

    Diagnostic Analytics

    Investigates reasons behind trends to determine why something happened.

    Predictive Analytics

    Uses historical data to forecast future trends and outcomes.

    Prescriptive Analytics

    Recommends actions to influence outcomes and make decisions.

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    Bias in Machine Learning

    The error due to overly simplistic assumptions in the learning algorithm.

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    Variance in Machine Learning

    The error due to excessive complexity in the model that captures noise.

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    Bias-Variance Tradeoff

    The compromise between bias and variance to minimize overall error.

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    Statistical Thinking

    Drawing inferences about a population from sample data; focuses on uncertainty and hypothesis testing.

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    Machine Learning Thinking

    An algorithmic approach for pattern recognition and predictions on new data.

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    Occam's Razor

    Principle suggesting that the simplest solution often is the best one.

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    Divergence

    A measure of difference between two probability distributions.

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    Machine Learning vs. Statistical Thinking

    Contrasts ML's focus on prediction and STATS on inference and relationships.

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    Outliers

    Data points that differ significantly from other observations.

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    Feature Selection

    Choosing important variables for improving model performance.

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    Prediction Accuracy

    The measure of how often the model makes correct predictions.

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    Hypothesis Testing

    A statistical method to test if a premise holds true.

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    Binary Classification

    A classification task with two possible outcomes.

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    Confusion Matrix

    A table used to evaluate the performance of a classification model.

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    Cross-Validation

    A technique for assessing how the outcomes of a statistical analysis will generalize to an independent data set.

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    Performance Metrics

    Quantifiable measures to evaluate the effectiveness of a model.

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    TP

    True Positive: Positive example correctly predicted as positive.

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    FN

    False Negative: Positive example incorrectly predicted as negative.

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    FP

    False Positive: Negative example incorrectly predicted as positive.

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    TN

    True Negative: Negative example correctly predicted as negative.

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    Decision Boundary

    A threshold that separates positive and negative predictions.

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    Accuracy

    The ratio of correct predictions to total observations.

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    Model Training

    The process of teaching a model using training data.

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    Threshold

    A probability cutoff to decide class prediction (e.g., 0.5).

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    ROC Curve

    A graph showing the performance of a classification model at various thresholds.

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    Study Notes

    Machine Learning 1 - Week 2 Lecture

    • Supervised machine learning was the focus of the lecture.

    Categories of Analytics

    • Analytics are categorized into different types:

      • Descriptive: Describes past performance (e.g., history, trends).
      • Diagnostic: Explains causes of trends.
      • Predictive: Forecasts future trends.
      • Prescriptive: Recommends actions.

    • Example questions for each category, regarding customer churn:

      • Descriptive: Which customer churned?
      • Diagnostic: Why did the customer churn?
      • Predictive: Which customers will churn?
      • Prescriptive: What can I do to change the outcome of customer churn?

    The Bias-Variance Tradeoff

    • Minimizing errors involves minimizing both bias and variance.

    • Variance is always non-negative; bias can be negative.

    • It's easier to optimize for one type of error, but optimizing for both (bias and variance) provides the best outcome.

    • A graph was shown of Mean Squared Error vs. Flexibility, outlining the tradeoff.

    • Some models are shown along the diagram of different types of algorithm.

    Statistical Learning

    • The error in prediction is a combination of reducible and irreducible errors.
    • Reducible error is due to the model's ability to learn patterns from data.
    • Irreducible error is due to factors outside the model, such as inherent randomness.

    Statistical vs. Machine Learning Thinking

    • Statistical thinking focuses on inference, uncertainty quantification, and assumptions validation.
    • Machine learning focuses on finding patterns in data to create accurate predictions on new, unseen data.
    • This can be done using an experimental approach, trying different algorithms and methods.

    Machine Learning vs. Statistical Thinking (Concepts)

    • Simplicity: Occam's razor – use the simplest model that works. Sometimes, increased complexity is needed.
    • Divergence: Data pre-processing, transformation, and model validation differs between statistical methods and machine learning approaches/methods.
    • Outliers: Machine learning focuses on how outliers affect predictions, while statistics focuses on interpretation.
    • Feature Selection: Machine learning selects features based on their impact on predictions, while statistics focuses on interpreting relationships.
    • Results: Machine learning emphasizes prediction accuracy, while statistics emphasizes inference and confidence intervals.

    Structure of Training and Prediction

    • Data is split into training and testing sets.
    • A model is created with parameters.
    • The model is trained using the training data.
    • Prediction is made on the testing data, and accuracy is measured using the testing data set.

    Threshold

    • Many models output probabilities.
    • A threshold is used to classify an observation based on the probability (e.g., above 0.5 predict as 1, below 0.5 predict as 0).
    • Experimenting with changing thresholds is helpful.
    • Choosing a threshold depends on considerations of importance of the different types of errors (false positives and false negatives).

    ROC Curve

    • ROC (Receiver Operating Characteristic) curves plot True Positive Rate (TPR) against False Positive Rate (FPR).
    • ROC curves show how TPR and FPR change as the threshold changes.
    • Area under the ROC curve (AUC) can serve as a measure of classifier performance, especially valuable with imbalanced datasets.
    • ROC curves can help determine the optimal threshold to minimize the costs of false predictions (false positive and false negative).

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    Machine Learning 1 Week 2 Lecture PDF

    Description

    This quiz covers the key concepts from the second week of the Machine Learning lecture. Topics include categories of analytics such as descriptive, diagnostic, predictive, and prescriptive analytics, along with the bias-variance tradeoff in supervised machine learning. Test your understanding of the lecture content and its applications.

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