Handbook of Hidden Data Scientist (Python)
  • Introduction
  • Machine Learning
    • Supervised Learning
      • Features and Labels
      • Linear Decision Surface
      • Naive Bayes
      • Support Vector Machine
      • Decision Trees
      • Regressions
  • Python
  • CSV with pandas
    • Reading CSV
    • Math Operations on Column
    • Joining CSVs
    • Plot and Normalize CSV Data
  • NumPy
    • Using NumPy from pandas DataFrame
    • Create NDArray
    • Working with NDArray
    • Timing operations
  • Statistical Analysis
    • Global Statistics
    • Rolling Statistics
    • Daily Returns
    • Cumulative Returns
  • Incomplete Data
    • Pandas fillna()
  • Histograms and Scatter Plots
    • Histogram
    • Two Histograms
    • Scatter Plot
  • Visualization
    • pyplot
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  1. Machine Learning
  2. Supervised Learning

Features and Labels

PreviousSupervised LearningNextLinear Decision Surface

Last updated 5 years ago

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Features of music could be: intensity, tempo, genre, gender, rhythm and so on. Look at this example. Features are expressed on axes and labels are the answers based on the coordinates of features. In this example, features are numerical values of intensity and tempo. Labels are classification of data points in the chart. Int his case, we have two labels, "She likes those" and "She doesn't like".

Here is another example with two features, bumpiness and slope. Labels, in this example, are the colors blue, red and green.