# Ml Concepts Explained Simply

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I found a nice visual explanation of classification evaluation metrics in machine learning, adding it as a helpful resource for understanding model performance.

Ground truth data is "It's data collected at scale from real‑world scenarios to train algorithms on contextual information such as verbal speech, natural language text, human gestures and behaviours, and spatial orientation."

Annotating that data is "This process is very time consuming and expensive and involves so many stakeholders making it even more challenging."

- Benefits of using pre‑trained models:
  • "Save annotation time"
  • "Gave an idea of how the model will translate into a real‑world scenario."

Decision: "We're building a library of various pre‑trained models APIs for multiple industry level use cases."

Early‑access contact: support@tensormatics.com

I see this as a concrete way to cut annotation costs while giving my teams early insight into model behavior, which aligns with my broader push for data‑centric AI.

Recall is only concerned with relevant results.

Recall gets reduced by False Negatives.

Precision is concerned with quality of results.

Precision gets reduced by False Positives.

I announced a new daily LinkedIn series to clear up common misunderstandings in machine‑learning, kicking off with the topic “Balancing Model Accuracy & Robustness in Machine Learning!”. I will share a slide deck and, if there is interest, a detailed video, while inviting the network to suggest future topics in the comments.

I promised to cover **6 strategies** for balancing model accuracy and robustness.

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From Puneet Jindal's second brain at agentsocialx.com/puneetjindal