Train/Test Method in Machine Learning

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Questions and Answers

What is the formula for calculating precision?

  • True Negative / (True Negative + False Positive)
  • True Positive / (True Positive + False Negative)
  • True Positive + False Positive
  • True Positive / (True Positive + False Positive) (correct)

Which metric focuses on predicting positive cases out of actual positives?

  • Sensitivity (correct)
  • Precision
  • Specificity
  • Accuracy

How is specificity defined in the context of evaluation metrics?

  • True Positive / (True Positive + True Negative)
  • False Positive / (True Negative + False Negative)
  • True Positive / (True Negative + False Negative)
  • True Negative / (True Negative + False Positive) (correct)

What does the F-score represent in model evaluation?

<p>The harmonic mean of precision and sensitivity (D)</p> Signup and view all the answers

Which of the following metrics does not evaluate correctly predicted negative cases?

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

What does True Positive (TP) indicate in a classification model?

<p>Predictions that are correctly identified as positive (A)</p> Signup and view all the answers

Which metric is defined as the ratio of the total number of correctly classified positive classes to the total number of predicted positive classes?

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

Why might accuracy be misleading in a classification model with imbalanced classes?

<p>It can still appear high if the model predicts the majority class for all instances. (C)</p> Signup and view all the answers

What does a False Positive (Type 1 Error) represent?

<p>A negative prediction incorrectly classified as positive (A)</p> Signup and view all the answers

What does a True Positive (TP) represent in the context of a confusion matrix?

<p>An animal predicted to be a cat that is actually a cat (B)</p> Signup and view all the answers

What is the purpose of the F1-Score in model evaluation?

<p>To provide a balance between precision and recall (A)</p> Signup and view all the answers

Which of the following refers to a Type 1 error?

<p>Predicting a cat when it is actually a dog (C)</p> Signup and view all the answers

Which of the following statements about Recall (True Positive Rate) is true?

<p>It measures the percentage of actual positives that were predicted correctly. (D)</p> Signup and view all the answers

What is the value of True Negatives (TN) in the provided confusion matrix example?

<p>11 (D)</p> Signup and view all the answers

In a confusion matrix, what does a False Negative (FN) signify?

<p>Predicting a negative result when the actual result is positive (D)</p> Signup and view all the answers

What does True Negative (TN) signify in a confusion matrix?

<p>The number of negative instances correctly classified as negative (C)</p> Signup and view all the answers

How is accuracy mathematically calculated?

<p>TP + TN / TP + TN + FP + FN (C)</p> Signup and view all the answers

What graphical representation is commonly used to visualize a confusion matrix?

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

If the predicted values contain 6 True Positives, what does this imply about the model's performance?

<p>It has failed to identify some positive cases. (C)</p> Signup and view all the answers

Which of the following options best describes False Positives in the context of the provided example?

<p>Predicting a cat when it's actually a dog (D)</p> Signup and view all the answers

What role does the confusion matrix serve in evaluating a predictive model?

<p>It identifies the number of incorrect predictions. (A)</p> Signup and view all the answers

What is the purpose of the plot() method in the context of the provided example?

<p>To visualize the relationship between two variables (C)</p> Signup and view all the answers

What does a high R-squared score, close to 1, indicate about a model?

<p>The model fits the data very well (C)</p> Signup and view all the answers

What does numpy.poly1d(numpy.polyfit(train_X, train_y, 4)) represent in the code?

<p>A polynomial regression model based on training data (D)</p> Signup and view all the answers

What does the term 'overfitting' imply in the context of the trained model?

<p>The model is too complex and does not generalize well (A)</p> Signup and view all the answers

Why is it necessary to split the dataset into training and testing sets?

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

What does the method r2_score() from the sklearn module measure?

<p>The goodness of fit between the actual and predicted values (A)</p> Signup and view all the answers

In the context of the example, what does the term 'test_X' represent?

<p>The subset of data for model evaluation after training (C)</p> Signup and view all the answers

If a model gives a result of 0.799 for the R-squared score, how would you interpret this?

<p>The model shows a moderate relationship with the data (A)</p> Signup and view all the answers

What is the formula for calculating Sensitivity (Recall)?

<p>True Positive / (True Positive + False Negative) (A)</p> Signup and view all the answers

Which term describes how well a model predicts negative results?

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

When should you rely on Precision as a performance metric?

<p>When False Positives are much more important (C)</p> Signup and view all the answers

Why might accuracy be misleading as a performance measure?

<p>It does not account for class imbalance (C)</p> Signup and view all the answers

Which function would you use to calculate Specificity in the provided context?

<p>metrics.recall_score with pos_label=0 (D)</p> Signup and view all the answers

In which situation is Recall particularly important to measure?

<p>When False Negatives are highly consequential (A)</p> Signup and view all the answers

What does the term 'Classification Report' provide in the context of model evaluation?

<p>Comprehensive metrics including precision, recall, and F1-score for each class (A)</p> Signup and view all the answers

What metric is better suited for evaluating models on balanced datasets?

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

What does an R2 score of 0.809 indicate about the model?

<p>The model fits the testing set well. (D)</p> Signup and view all the answers

In the context of a confusion matrix, what represents a True Positive (TP)?

<p>Actual Positive predicted as Positive. (A)</p> Signup and view all the answers

Which element is NOT part of a confusion matrix?

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

What would be the prediction if the input variable is 5 minutes based on the example?

<p>22.88 dollars (B)</p> Signup and view all the answers

What is the role of the rows in a confusion matrix?

<p>They represent the actual classes the outcomes should have been. (B)</p> Signup and view all the answers

Which of the following statements about False Negatives (FN) is true?

<p>They denote when the actual is Positive but predicted as Negative. (D)</p> Signup and view all the answers

Why is R2 score important in model evaluation?

<p>It quantifies how well the model predictions match actual outcomes. (A)</p> Signup and view all the answers

What does a False Positive (FP) indicate?

<p>The actual class is Negative but predicted as Positive. (B)</p> Signup and view all the answers

Flashcards

plt.plot()

A method in the matplotlib module used to draw a line through data points in a plot.

Polynomial Regression

A statistical technique that uses a polynomial function to model the relationship between variables.

Training Data

The portion of the data used to train the model.

Testing Data

The portion of the data used to evaluate the model's performance on unseen data.

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R-squared (R²)

A measure of how well the model's predictions align with the actual values.

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r2_score()

A method in the sklearn module that calculates the R-squared score for a given model and data.

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Overfitting

A situation where a model performs well on the training data but poorly on unseen data.

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Prediction

The process of using the trained model to predict outcomes for new data.

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R-squared Score (R²)

A statistical measure that indicates how well a regression model fits the observed data.

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Confusion Matrix

A technique used to assess the performance of a classification model by comparing predicted values to actual values.

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False Positive (FP)

A situation where the model predicts a positive outcome, but the actual outcome is negative. It's also known as a Type 1 error.

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False Negative (FN)

A situation where the model predicts a negative outcome, but the actual outcome is positive. It's also known as a Type 2 error.

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Recall

A metric used to measure the proportion of actual positive cases correctly predicted by the model.

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F1-score

A measure combines precision and recall to balance the trade-off between identifying true positives and minimizing false positives.

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Train/Test Split

The process of dividing a dataset into training and testing sets to evaluate the performance of a model.

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True Positive (TP)

The number of correctly predicted positive cases.

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True Negative (TN)

The number of correctly predicted negative cases.

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Actual Positive Count

The combination of True Positives and False Negatives. Represents the total number of actual positive cases.

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Actual Negative Count

The combination of True Negatives and False Positives. Represents the total number of actual negative cases.

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Predicted Positive Count

The sum of True Positives and False Positives. Represents the total number of predicted positive cases.

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Sensitivity (Recall)

A metric that measures the proportion of actual positive cases that are correctly identified as positive.

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Specificity

A metric that measures the proportion of actual negative cases that are correctly identified as negative.

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Accuracy

A metric that tells you how well your model is performing overall. It's calculated as the percentage of correctly classified instances out of all instances.

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Classification Report

The calculation of all performance metrics, including precision, recall, f1-score, and support for each class.

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Recall (Sensitivity)

The proportion of correctly classified positive cases out of all the actual positive cases. It measures how well the model identifies positive cases.

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Study Notes

Train/Test Method in Machine Learning

  • Train/Test is a method used to evaluate the accuracy of a machine learning model.
  • Data is split into two sets: training and testing.
  • Training data is used to create/train the model.
  • Testing data is used to evaluate the model's accuracy on unseen data.

Example Data Set

  • Data set illustrates 100 customers and their shopping habits.
  • X-axis represents minutes before purchase.
  • Y-axis represents amount spent on purchase.

Splitting Data

  • Training set (80%): A random selection of the original dataset.
  • Testing set (20%): The remaining portion of the data.

Fitting the Data

  • Polynomial regression is suggested as a possible model fit to determine relationship between time spent and money spent.
  • Code example uses numpy and matplotlib to create and display this line.

R-squared Score

  • R-squared (R2) measures the relationship between x and y.
  • Ranges from 0 (no relationship) to 1 (perfect relationship).
  • sklearn's r2_score() function used to calculate relationship in the data.
  • R2 score calculated for both training and testing sets.

Predicting Values

  • Use the trained polynomial model to predict values for new input values.
  • Example shows how to predict amount spent for a customer staying 5 minutes in store.

Confusion Matrix

  • Used for classification problems.
  • Rows represent actual classes, while columns represent predicted classes.
  • Identifies where errors in the model occur.
  • Possible to generate confusion matrix from a logistic regression or other classification models.

Confusion Matrix Metrics

  • TP (True Positive): Predicted positive, actual positive.
  • TN (True Negative): Predicted negative, actual negative.
  • FP (False Positive): Predicted positive, actual negative (Type 1 error).
  • FN (False Negative): Predicted negative, actual positive (Type 2 error).

Classification Performance Metrics

  • Accuracy: The ratio of correct predictions to total predictions.
  • Precision: Predictive accuracy on positive cases. Percentage of correctly predicted positive instances.
  • Recall (Sensitivity): Percentage of correctly predicted positive cases out of actual positive cases. Percentage of actual positive cases correctly predicted.
  • Specificity: Correctly predicted negative cases percentage out of actual negative cases.
  • F1-score: Harmonic mean of precision and recall. A balance between precision and recall.
  • Code Examples: Demonstrate how to calculate these using Python's sklearn library.

Choosing the Right Metric

  • Select the most appropriate metric (accuracy, precision, recall, F1-score) based on the specific needs of the problem.
  • Accuracy important for balanced datasets.
  • Precision useful when FP errors are more important than FN errors.
  • Recall (sensitivity) more important if FN errors are more important.
  • F1-score best when both FP and FN count.

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