RRepoGEO

REPOGEO REPORT · LITE

wangyuGithub01/Machine_Learning_Resources

Default branch master · commit b9a65f73 · scanned 5/13/2026, 8:03:39 AM

GitHub: 1,236 stars · 182 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 wangyuGithub01/Machine_Learning_Resources, 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
    Clarify the README's opening to emphasize interview preparation

    Why:

    CURRENT
    :point_right: 此repo主要是为了整理机器学习面试相关知识点的有用链接
    COPY-PASTE FIX
    ## 🚀 机器学习面试复习资源 (Machine Learning Interview Preparation Resources)

:point_right: 此repo旨在整理机器学习面试相关的核心知识点、算法和实践技巧的有用链接，帮助准备面试的同学高效复习。
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    machine-learning, ml, interview-preparation, interview-questions, machine-learning-algorithms, feature-engineering, nlp, deep-learning, data-science, study-guide, resources
  • highlicense#3
    Add a standard open-source license file

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a `LICENSE` file in the root of the repository and add the text for the MIT License (or another suitable open-source license).

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 wangyuGithub01/Machine_Learning_Resources
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
An Introduction to Statistical Learning with Applications in R
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. An Introduction to Statistical Learning with Applications in R · recommended 1×
  2. The Elements of Statistical Learning · recommended 1×
  3. Andrew Ng's Machine Learning Course (Coursera) · recommended 1×
  4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow · recommended 1×
  5. Scikit-Learn · recommended 1×
  • CATEGORY QUERY
    I need to review core machine learning concepts and algorithms for upcoming interviews.
    you: not recommended
    AI recommended (in order):
    1. An Introduction to Statistical Learning with Applications in R
    2. The Elements of Statistical Learning
    3. Andrew Ng's Machine Learning Course (Coursera)
    4. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    5. Scikit-Learn
    6. Keras
    7. TensorFlow
    8. Machine Learning Crash Course with TensorFlow APIs (Google Developers)
    9. TensorFlow APIs
    10. Deep Learning

    AI recommended 10 alternatives but never named wangyuGithub01/Machine_Learning_Resources. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practices for feature engineering and model optimization in machine learning?
    you: not recommended
    AI recommended (in order):
    1. Pandas Profiling
    2. Sweetviz
    3. StandardScaler
    4. MinMaxScaler
    5. RobustScaler
    6. KBinsDiscretizer
    7. OneHotEncoder
    8. LabelEncoder
    9. OrdinalEncoder
    10. Category Encoders
    11. PolynomialFeatures
    12. TfidfVectorizer
    13. Word2Vec
    14. GloVe
    15. FastText
    16. Gensim
    17. BERT
    18. GPT-3/4
    19. SelectKBest
    20. SelectPercentile
    21. RFE
    22. XGBoost
    23. LightGBM
    24. CatBoost
    25. Random Forest
    26. GridSearchCV
    27. RandomizedSearchCV
    28. Hyperopt
    29. Optuna
    30. Scikit-optimize
    31. Adam
    32. RMSprop
    33. SGD with Momentum
    34. KFold
    35. StratifiedKFold
    36. TimeSeriesSplit
    37. SMOTE
    38. SHAP
    39. LIME

    AI recommended 39 alternatives but never named wangyuGithub01/Machine_Learning_Resources. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 wangyuGithub01/Machine_Learning_Resources?
    pass
    AI did not name wangyuGithub01/Machine_Learning_Resources — 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?

  • If a team adopts wangyuGithub01/Machine_Learning_Resources in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wangyuGithub01/Machine_Learning_Resources 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 wangyuGithub01/Machine_Learning_Resources solve, and who is the primary audience?
    pass
    AI did not name wangyuGithub01/Machine_Learning_Resources — 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?

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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