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AUC β€” one number to summarize your ROC curve. A classification model is judged not just at one threshold, but across all thresholds. ROC curve shows the tradeoff between catching positives and avoidi
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ROC Curve β€” visualizing your model across every threshold. A classification model doesn't output a class directly. It outputs a probability. You set a threshold β€” above it, Positive. Below it, Negativ
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F1 Score A balanced view of Precision and Recall. F1 = 2 Γ— (Precision Γ— Recall) / (Precision + Recall) Harmonic Mean of Precision and Recall Harmonic Mean rule: One value low β†’ F1 will be low Both val
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Precision answers β€” out of everything the model predicted as positive, how many were actually positive? Precision = TP divided by TP plus FP. High precision means β€” when model says positive, it's usua
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Accuracy tells you how many errors. Confusion Matrix tells you what kind. TP β€” Predicted Positive, Actually Positive. TN β€” Predicted Negative, Actually Negative. FP β€” Predicted Positive, Actually Ne
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Classification - Teaching machines to make decisions. Not every problem needs a number as an answer. Sometimes you just need a label. Spam or Not Spam. The model learns patterns from data. When it
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Gradient Descent β€” Part 3 In Part 2 we saw GD gets stuck in local minima. The fix? Stochastic Gradient Descent. Instead of using the entire dataset per step β€” SGD uses one random data point at a ti
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Gradient Descent -- Part 2 In Part 1 we asked to questions. 1.How do we know we've actually hit the minimum? 2.What if the loss curve has more than 1 minimum? When Slope = 0, model stops. That'
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Gradient Descent -- Part 1 How does Machine Learning model actually improve itself? Start at a random point on the Loss curve. Check the slope. Move in the opposite direction. Repeat until you h
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Polynomial Regression -- when straight line just isn't enough. Sometimes data curves, linear regression draw a straight line and miss the pattern entirely. Polynomial Regression fit a curve instead --
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Underfitting occurs when model is too simple to understand underlying important patterns and relationships in data. Which leads to poor performance on both training and test data. #machinelearnings
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About @creasyml

creasyml is a Instagram nano influencer. With 2.4K followers and 11 posts, creasyml's account shows a follower-to-following ratio of 1189.5:1. View all content anonymously on Pictame - no login required.

creasyml's engagement rate sits at 0.06%. For accounts with 1K-10K followers, the average is around 5%, so there's some room to grow β€” though every community is unique.

Out of the last 11 posts, 100% are videos and 0% are photos. Content shows up about every 5 days. Captions lean detailed, averaging 509 characters.

0.06% engagement rate β€” room to grow compared to average for 1K-10K follower accounts.
Posts about every 5 days.
1189.5:1 follower-to-following ratio.

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@creasyml

creasyml

Public
Posts
11
Followers
2.4K
Top Hashtags
#learnmachinelearning#classification#precision#recall#gradientdescent#optimization#machinelearningsimplified#f1score

Explore the most impactful hashtags used in recent posts. These tags highlight the core interests and trending topics that define this profile's digital influence and content strategy.

Following
2
Avg. Likes
-1
Avg. Comments
2
Engagement
0.04%

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