Logo J. Marcus Hughes
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    • Posts
    • Welcome
    • Anomaly Detection
      • Atypicality
      • Axis-aligned RRCF artifacts
      • goal of nonstationary
      • Mostly noise training
    • Assorted Coding
      • flood
      • inclusion check
      • mood tracking
      • np.sum speeds in Cython
      • sliding puzzles
      • truncating numbers
      • Ulam Warburton
    • Assorted ML
      • fake APOD generation
      • TSS vs f1
    • Denoising
      • denoising autoencoder
    • Paper Reading
      • State of Astro-informatics
    • Photography
      • New Instagram
    • Solar
      • AIA temporal coloring
      • EUI artifact
      • HEK thematic maps
      • SUVI calendar heatmap
      • Thematic Map Paper
    • Teaching
      • peer review activity
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    Creating fake APOD pictures

    Introduction I volunteer to teach programming to two high school students through a non-profit called The Coding School, specifically the CodeConnects program. One of my students is taking AP Computer Science at the same time. His teacher requires all the students to complete a final project of their choosing; they have complete freedom in picking the project except it has to be approved by the teacher. (That sounds like an awesome teacher by the way, encouraging students to learn on their own by pursuing their own passion projects.

    May 30, 2022 Read
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    TSS versus f1-measure

    The above movie shows how accuracy, TSS, and f1-measure change under the assumption that a classifier has no false positives until it has classified all of a class correctly. The vertical grey line shows the actual percentage of the features having a given class versus the horizontal axis what percentage of the class is identified by the model. For example, if the true class percentage is 0.1 as shown below we see that an aggressive classifier, one that prefers creating false positives, is punished much less by accuracy and TSS than by the f1-measure.

    March 27, 2019 Read
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    Contact me:
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