Monitor: test_user_025

Open JSON
Relational DB – Profile
Name: test_user_025
Domain:
Thread ID: thread_m7h1tse4v
Updated: 2026-01-08T00:06:56
Skills:
Strengths:
Clear structure Identified factors Technical accuracy
Weaknesses:
Depth lacking Incomplete answers Irrelevance Lack of focus Lack of knowledge Lacks specificity Needs structure Poor structure
Categories:
Raw (users row)
{
  "categories": [],
  "domain": null,
  "name": "test_user_025",
  "skills": [],
  "strengths": [
    "Clear structure",
    "Identified factors",
    "Technical accuracy"
  ],
  "thread_id": "thread_m7h1tse4v",
  "updated_at": "2026-01-08T00:06:56",
  "user_id": "test_user_025",
  "weaknesses": [
    "Depth lacking",
    "Incomplete answers",
    "Irrelevance",
    "Lack of focus",
    "Lack of knowledge",
    "Lacks specificity",
    "Needs structure",
    "Poor structure"
  ]
}
Relational DB – Academic Summary
Attempts 6
Technical Accuracy 2.93/10
Reasoning Depth 2.89/10
Communication Clarity 2.94/10
Overall (hybrid) 2.51/10
Raw (academic_summary row)
{
  "communication_clarity": 2.94,
  "question_attempted": 6,
  "reasoning_depth": 2.89,
  "score_overall": 2.51,
  "technical_accuracy": 2.93,
  "user_id": "test_user_025"
}
Vector DB – Profile Snapshot
Vector: test_user_025 (profiles_v1)
Domain: unknown
Skills:
Strengths:
Technical accuracy Clear structure
Weaknesses:
Depth lacking
User Summary:
Raw (vector metadata)
{
  "domain": "unknown",
  "skills": [],
  "strengths": [
    "Technical accuracy",
    "Clear structure"
  ],
  "type": "profile_snapshot",
  "updated_at": 1767830821,
  "user_id": "test_user_025",
  "user_summary": "",
  "version": "v3",
  "weaknesses": [
    "Depth lacking"
  ]
}
Runtime State (LangGraph)
Graph State:
Strategy:
Question:
Answer:
Metrics:
  • TA: — —
  • RD: — —
  • CC: — —
Combined Score:
Overall Feedback:
Raw (full state)
{}

Click Start to initialize a new run for this user/thread.

Conversation History (Relational DB)
Total entries: 6
# Time QID Overall TA RD CC
1 2025-12-29T03:38:08 data_cleaning_98e6e04c23 0.00 0.00 0.00 1.00
Question: What is type of your dataset?
Answer: That's it
2 2026-01-07T23:49:59 scikit_learn_76425b6b8d 0.00 0.00 0.00 1.00
Question: What is out of core learning?
Answer: First of all, I'm lo currently pursuing my core in artificial intelligence and data science.
3 2026-01-07T23:51:57 algorithm_selection_c98a1d08c5 1.00 1.00 1.00 1.00
Question: Suppose you are trying to solve a classification problem; how do you decide which algorithm to use? Give scenarios.
Answer: In classification from like uh, when we are classifying the breast cancer reduction be like uh bin and.
4 2026-01-07T23:57:23 algorithm_selection_c98a1d08c5 3.00 3.00 3.00 2.00
Question: Suppose you are trying to solve a classification problem; how do you decide which algorithm to use? Give scenarios.
Answer: When solving the Classification Pro problem, I decided to check whether the data check whether the following the data characteristics, problem complexity, integration needs and.
5 2026-01-08T00:02:59 algorithm_selection_c98a1d08c5 4.00 4.00 5.00 4.00
Question: Suppose you are trying to solve a classification problem; how do you decide which algorithm to use? Give scenarios.
Answer: When I solving the classification problem, I decide the problem cont contains some data characteristics, problem complexity, integrative needs and performance requirements. When considering the classification problem, we have to check the five checklist.
6 2026-01-08T00:06:54 image_classification_46210c9e54 9.00 9.00 8.00 9.00
Question: List down algorithms for segmentation
Answer: K-Means Clustering Groups pixels into K clusters Example: Color-based segmentation Mean Shift Density-based clustering Example: Object tracking Gaussian Mixture Model (GMM) Probabilistic clustering Example: Soft segmentation 🔹 Graph-Based Methods Graph Cut Segmentation as energy minimization Example: Foreground-background separation Normalized Cuts Graph partitioning approach Example: Complex scene segmentation 🔹 Deep Learning-Based Methods (VERY IMPORTANT) Fully Convolutional Networks (FCN) End-to-end pixel-wise segmentation U-Net Encoder-decoder architecture Example: Medical image segmentation SegNet Efficient encoder-decoder network DeepLab (v1–v3+) Uses atrous convolution Example: Semantic segmentation Mask R-CNN Instance segmentation Example: Object-level masks 2️⃣ TEXT / NLP SEGMENTATION Rule-Based Segmentation Uses predefined rules Example: Sentence segmentation Topic Modeling (LDA) Groups text into topics Example: Document segmentation Clustering (K-Means, Hierarchical) Groups similar text segments

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