Patrick Tinz

about 2 months ago

🚨 NEW AI Engineering Community Join our Community server and connect with other data scientists, AI engineers, and software developers. Share your knowledge, ask questions, and receive expert tips on AI, machine learning, and coding. Join for free: https://t.co/TAn7g5iZd6

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2 months ago

SQL Joins Cheat Sheet 🔥

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I was with Alibaba as a senior data scientist , and now I am the chief architect in the big data field at a major brokerage firm in China.

2 months ago

Docker Quick Tips

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Who to follow

Peng Qian

@qtalen

Towards Finance

@towardsfinance

Your home for finance. An innovation platform about business and investing. | No financial advice

Data Scientists | We write about AI, Software Engineering and Investment Research. | Founder @towardsfinance

patricktinz retweeted

about 2 months ago

Statistical bootstrap method and the central limit theorem Here's an easy-to-understand explanation: https://t.co/jyOwg4nh7U

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about 2 months ago

Subscribe to our blog for super-detailed articles and get all our FREE ML cheat sheets in a single PDF: https://t.co/TPu1MIODJJ

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8 months ago

Retrieval Augmented Generation (RAG) Visually Explained 🧐

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8 months ago

ROC plot - Clearly explained 👇🏽 💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR). 🤔 Specificity and Sensitivity The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity. Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part. 🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure. 😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1). A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector. 📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5. --- If you found it insightful, repost it with your network. Find us → @tinztwins Every day, we share tutorials and insights on ML, LLMs, and AI Agents.

tinztwins's tweet photo. ROC plot - Clearly explained 👇🏽

💡 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).

🤔 Specificity and Sensitivity

The True positive rate is also called sensitivity, and the True negative rate (TNR) is called specificity.

Specificity is a measure for the whole negative part of a data set, while sensitivity is a measure for the whole positive part.

🤖 The ROC plot uses the True positive rate (TPR) on the y-axis, and the false positive rate (FPR) is on the x-axis (formula FPR = 1 - TNR). You see a visual explanation in the figure.

😎 To interpret the ROC curve, note that a classifier with a random performance level is a straight line from the origin (0, 0) to the top right corner (1, 1).

A poor classifier lies below this line, and a classifier improves as it deviates upward from the bisector.

📊 Another criterion in the ROC curve is the area under the ROC curve (AUC) score. Here, we calculate the area under the curve. A good classifier has an AUC-Score > 0.5.

---

If you found it insightful, repost it with your network.

Find us → @tinztwins

Every day, we share tutorials and insights on ML, LLMs, and AI Agents.

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8 months ago

Do you want to learn how to implement an artificial neural network from scratch using NumPy? Learn more in our FREE super-detailed guide. https://t.co/obhw8b22Pu

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9 months ago

👀 https://t.co/Jvp3bP8qp4

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8 months ago

Agentic AI Design Patterns: Visually explained 🔥

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Patrick Tinz @patricktinz

9 months ago

It’s like a USB-C port for AI systems. MCP is more than just hype; it will be a crucial part of almost every software product in the coming years. Link to our Python guide: https://t.co/HmRZqJjEGl

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9 months ago

👀👇🏽

9 months ago

🚀 Expose Your Agno Agent as an AG-UI Compatible App! AG-UI (Agent-User Interaction Protocol) standardizes how AI agents connect to frontend applications. It’s like a universal translator for AI-driven systems. https://t.co/vI4oyL3nNu

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9 months ago

Boxplot: A clear explanation 👇🏽

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Towards Finance @towardsfinance

9 months ago

Let’s build a Local AI Agent with Knowledge and Storage Using @AgnoAgi 🚀 - 100% Local and Open Source - Agno to implement the AI agent - Agno Agent UI as an intuitive Agent interface Link to our FREE guide in the comments.

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10 months ago

Ledger Nano X Review 2025: The Best Crypto Wallet? | by @tinztwinsinvest https://t.co/2kTGvmLcit

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10 months ago

GitHub repo about LLMs and AI agents!

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10 months ago

Precision-Recall plot - Clearly explained 🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall. 🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions. The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure. 🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases. 💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line. 🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3. 📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.

tinztwins's tweet photo. Precision-Recall plot - Clearly explained

🔍 The precision-recall plot is a model-wide measure for evaluating classifiers. The plot is based on the evaluation metrics of Precision and Recall.

🧐 Recall (identical to sensitivity) is a measure of the whole positive part of a dataset, whereas precision is a measure of positive predictions.

The precision-recall plot uses precision on the y-axis and recall on the x-axis. You see a visual explanation in the figure.

🤔 It is easy to interpret a precision-recall plot. In general, precision decreases as recall increases. Conversely, as precision increases, recall decreases.

💡 A random classifier lies on the y-axis (precision) at y = P/( P + N ) (P: number of positive labels, N: number of negative labels). A poor classifier lies below this line, and a good classifier lies well above this line.

🌟 You can see two different plots in the figure. On the left side, you see the random line is y=0.5. The ratio of positives (P) and negatives (N) is 1:1. On the right side, you see the random line is y=0.25. There, we have a ratio of positives and negatives of 1:3.

📊 Another quality criterion in the precision-recall plot is the area under the curve (AUC) score, where the area under the curve is calculated. An AUC score close to 1 characterizes a good classifier.

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