RRepoGEO

REPOGEO REPORT · LITE

nosuggest/Reflection_Summary

Default branch master · commit 364216d6 · scanned 6/25/2026, 8:18:20 PM

GitHub: 2,567 stars · 496 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface nosuggest/Reflection_Summary, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to clarify actual content

    Why:

    CURRENT
    # Reflection_Summary
    COPY-PASTE FIX
    # Reflection_Summary: 算法理论基础知识应知应会
This repository serves as a comprehensive collection of fundamental theoretical knowledge for algorithms and machine learning, designed to be a go-to resource for essential concepts. It addresses key topics such as bias-variance trade-off, generative vs. discriminative models, probability, and AutoML.
  • hightopics#2
    Add comprehensive topics for better categorization

    Why:

    COPY-PASTE FIX
    machine-learning, deep-learning, algorithms, artificial-intelligence, theoretical-computer-science, data-science, statistics, probability, ml-theory, computer-science-education, interview-preparation
  • mediumabout#3
    Enhance the repository's 'About' description with English translation

    Why:

    CURRENT
    算法理论基础知识应知应会
    COPY-PASTE FIX
    算法理论基础知识应知应会 | Comprehensive theoretical knowledge for algorithms and machine learning, covering fundamental concepts like bias-variance, generative models, and probability. Ideal for study and interview prep.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface nosuggest/Reflection_Summary
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction · recommended 1×
  2. Deep Learning · recommended 1×
  3. Pattern Recognition and Machine Learning · recommended 1×
  4. Machine Learning: A Probabilistic Perspective · recommended 1×
  5. Andrew Ng's Machine Learning Course · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive explanations of fundamental machine learning theoretical concepts?
    you: not recommended
    AI recommended (in order):
    1. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
    2. Deep Learning
    3. Pattern Recognition and Machine Learning
    4. Machine Learning: A Probabilistic Perspective
    5. Andrew Ng's Machine Learning Course
    6. An Introduction to Statistical Learning with Applications in R

    AI recommended 6 alternatives but never named nosuggest/Reflection_Summary. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to effectively address high bias and variance issues in machine learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow (tensorflow/tensorflow)
    2. PyTorch (pytorch/pytorch)
    3. scikit-learn (scikit-learn/scikit-learn)
    4. Pandas (pandas-dev/pandas)
    5. XGBoost (dmlc/xgboost)
    6. LightGBM (microsoft/LightGBM)
    7. Albumentations (albumentations-team/albumentations)
    8. NLTK (nltk/nltk)
    9. Keras (keras-team/keras)
    10. CatBoost (catboost/catboost)
    11. Optuna (optuna/optuna)
    12. Hyperopt (hyperopt/hyperopt)
    13. matplotlib (matplotlib/matplotlib)
    14. seaborn (mwaskom/seaborn)

    AI recommended 14 alternatives but never named nosuggest/Reflection_Summary. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of nosuggest/Reflection_Summary?
    pass
    AI named nosuggest/Reflection_Summary explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts nosuggest/Reflection_Summary in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nosuggest/Reflection_Summary explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo nosuggest/Reflection_Summary solve, and who is the primary audience?
    pass
    AI did not name nosuggest/Reflection_Summary — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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nosuggest/Reflection_Summary — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite