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@bsouleyciv:

💫BSOULEY_CIV💫
💫BSOULEY_CIV💫
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Region: FR
Friday 21 August 2026 20:29:47 GMT
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Comments

cellghassan
cellghassan :
Nice words
2026-08-24 16:34:22
0
brigitte.poitevin8
Brigitte Poitevin :
amine protège moi seigneur
2026-08-23 07:48:46
1
mariame.sylla774
Mariame Sylla :
Qu’Allah nous protège pardonne nos comportements amina yaraby amina yaraby amina yaraby
2026-08-22 19:38:37
1
user4400320113318
user4400320113318 :
qu'Allah nous pardonne
2026-08-22 22:36:30
2
salma.bijoux8
Salma Bijoux :
amine
2026-08-24 07:03:36
0
simbas60
Simba :
Amine
2026-08-24 11:38:50
0
emma.hami
Emma Hami :
amine
2026-08-22 19:00:04
0
mham314
M'hamed :
2026-08-23 11:58:58
0
kokoifred.kokoifr7
Kokoifred Kokoifred :
🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰
2026-08-22 18:38:38
0
alhamidi.abdou.ta
Alhamidi Abdou tabibou :
soidakta
2026-08-22 21:00:45
0
djamila.adjaoubon
dame kankou :
🥰🤲🤲🤲🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🥰🤲🤲🤲🤲🤲🤲
2026-08-21 21:23:10
0
hafisathbouraima
Irène Alicia ♥️😍 :
ameinm
2026-08-22 22:33:59
0
ganame.rd
GANAME RD :
🥰
2026-08-21 21:05:16
0
alasan.adamou
Alasan Adamou :
🤲🤲🤲
2026-08-24 20:28:53
0
yasya.amadou92
Yasya Amadou :
🥰🥰🥰
2026-08-24 19:45:42
0
.mme.la_juge_77
ℳ𝓂ℯ．𝓁𝒶．𝒿𝓊𝑔ℯ💞 :
🤲🤲❤️❤️
2026-08-23 18:26:35
0
sarankante826
Saran ❤️🩶🤍🫶 :
👌👌👌
2026-08-22 21:31:23
0
leyloxerow
Aragsan waaberi ✌️ :
❤️❤️❤️
2026-08-22 19:11:26
0
ablo.rd
Ablo sarki :
🥰🥰🥰
2026-08-22 17:48:41
0
nassirinaissouf
Nassirina Issouf :
🥰🥰🥰
2026-08-22 17:25:30
0
calabrese.calabre
Calabrese Calabrese :
♥️♥️♥️
2026-08-22 16:01:50
0
med05536
Mamma Cissé..😍🐒🕵‍♀️ :
🥰🥰🥰
2026-08-22 14:40:54
0
idi.gosiel1
idrissa BvB :
🥰🥰🥰
2026-08-22 10:44:06
0
hamed.bamba337
Hamed Bamba :
🥰🥰🥰
2026-08-21 20:34:22
0
To see more videos from user @bsouleyciv, please go to the Tikwm homepage.

Other Videos

Confusion Matrix and Performance Metrics Evaluation in Python Detailed exploration of confusion matrices, performance metrics, and error analysis techniques using Python. Learn to assess model performance through accuracy, precision, recall, F1 score, and ROC curves. Discover how to implement these metrics with practical code examples and real-world applications. You can find, for free, this and all others slideshow on the xbe.at website. #python #datascience #machinelearning #stem #programming #coding #computerscience #dataanalysis #statistics #Tech Tips to reinforce your understanding of confusion matrices and metrics: 1. Always validate your metrics implementation. Create small test cases where you can manually calculate the expected values and compare them with your code's output. This helps catch subtle bugs and builds intuition. 2. Experiment with different thresholds. Classification metrics often depend on probability thresholds - try various values and observe how they affect your model's performance trade-offs. 3. Visualize everything. Create plots for your confusion matrices, ROC curves, and precision-recall curves. Visual feedback helps identify patterns and issues more quickly than raw numbers. 4. Document your evaluation choices. Keep clear notes about which metrics you chose and why, especially for imbalanced datasets where accuracy alone might be misleading. 5. Study the relationships between metrics. Understand how precision and recall trade off, how accuracy can be misleading with imbalanced data, and when to use each metric. This deeper understanding is crucial for real-world applications.
Confusion Matrix and Performance Metrics Evaluation in Python Detailed exploration of confusion matrices, performance metrics, and error analysis techniques using Python. Learn to assess model performance through accuracy, precision, recall, F1 score, and ROC curves. Discover how to implement these metrics with practical code examples and real-world applications. You can find, for free, this and all others slideshow on the xbe.at website. #python #datascience #machinelearning #stem #programming #coding #computerscience #dataanalysis #statistics #Tech Tips to reinforce your understanding of confusion matrices and metrics: 1. Always validate your metrics implementation. Create small test cases where you can manually calculate the expected values and compare them with your code's output. This helps catch subtle bugs and builds intuition. 2. Experiment with different thresholds. Classification metrics often depend on probability thresholds - try various values and observe how they affect your model's performance trade-offs. 3. Visualize everything. Create plots for your confusion matrices, ROC curves, and precision-recall curves. Visual feedback helps identify patterns and issues more quickly than raw numbers. 4. Document your evaluation choices. Keep clear notes about which metrics you chose and why, especially for imbalanced datasets where accuracy alone might be misleading. 5. Study the relationships between metrics. Understand how precision and recall trade off, how accuracy can be misleading with imbalanced data, and when to use each metric. This deeper understanding is crucial for real-world applications.
Sunce, dobra ekipa i novi LANEIGE favoriti za usne? Ako nas pitaš, tako izgleda ljetni mood. ☀️🩷 @Kate #bipahrvatska #LANEIGE
Sunce, dobra ekipa i novi LANEIGE favoriti za usne? Ako nas pitaš, tako izgleda ljetni mood. ☀️🩷 @Kate #bipahrvatska #LANEIGE
热缩膜这样用 #Goodthing #MustHave #LifeHack #fyp
热缩膜这样用 #Goodthing #MustHave #LifeHack #fyp
#恋愛 #恋愛心理学 #本命
#恋愛 #恋愛心理学 #本命
E se você quer saber como os brabos da @intzesports fazem acontecer… o ADM abriu a call. 📢 Dá o play e sente a queda dos INTRÉPIDOS. 🚀 #FreeFire #FFWSBR
E se você quer saber como os brabos da @intzesports fazem acontecer… o ADM abriu a call. 📢 Dá o play e sente a queda dos INTRÉPIDOS. 🚀 #FreeFire #FFWSBR
アプデで増えた設定！#荒野行動 #荒野女子 #荒野の光 #おすすめ #荒野行動純金配布
アプデで増えた設定！#荒野行動 #荒野女子 #荒野の光 #おすすめ #荒野行動純金配布

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