Introduction to Machine Learning

dougieduk·2022년 3월 30일

1. Supervised Learning Overview

  • trained using labeled examples, such as an input where the desired output is known
  • For example, a segment of text could have a category label, such as
    - Spam vs Legitimate Email
    - Positive vs Negative review
  • Network receives a set of inputs along with the corresponding correct outputs, and the algorithms learns by comparing its outputs
  • likely to predict future events
  • Data is often split into 3 sets
    - Training Data : train the model parameters
    - Validatation Data : determined what model hyperparameters to adjust
    - Test Data : get some true final performance metric
    • You cannot tweak the parameters based on the Test data

2. Evaluating Performance - Classification Error Metrics

  • Once we have the model's predictions from the X_test data, we compare it to the true y data.
  • We could organize our predicted values compared to the real values in a confusion matrix


  • Accuracy in classification problems is the number of correct predictions made by the model divided by the total number of predictions
  • useful when target classes are well balanced (e.g. equal ratio of dog and cat images)
    - not a good choice with unbalanced classes (e.g. 99% of images are dogs)


  • ability of a model to find all the relevant cases within a dataset
  • (true positives)/ (true positives + false negatives)


  • ability of a classification model to identify only the relevant data points
  • (true positives) / (true positives + false positives)


  • Optimal blend of precision and recall
  • it punishes extreme values
    - precision 1.0 recall 0.0 => average of 0.5 but F1 of 0

What is a good enough accuracy?

  • Do we have a balanced population?
  • What is the goal of the model?
    - is it to fix false positives?
    - is it to fix false negatives? (disease diagnosis)
  • It all depends on the context of the problem

3. Evaluating Performance - Regression Error Metrics

Overall concept

  • recall and precision might not be useful
  • regression models are composed of continuous variables
  • Common metrics used
    - Mean Absolute Error
    - Mean Squared Error
    - Root Mean Square Error

Mean Absolute Error (MAE)

  • This is the mean of the absolute value of errors
  • Easy to understand
  • Shortcoming: MAE won't punish large errors

Mean Squared Error (MSE)

  • This is the mean of the squared errors
  • Larger errors are noted more than with MAE, Making MSE more popular
  • Shortcoming: metrics can be distorted

Root Mean Square Error (RMSE)

  • This is the root of the mean of the squared errors
  • Most Popular (has same units as y)

The Contents of this post belongs to Jose Portilla's Python for Data Science and Machine Learning Bootcamp

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