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Just solved all 30 Days of Code challenges in Tutorials on @HackerRank. Try it out! https://t.co/xi7yl1Vcvd #programming

Next steps: Multiclass classification on MNIST ROC–AUC analysis across models Threshold tuning for optimal performance Ever onward, steady and curious. #MNIST #MLJourney #AIResearch #Learning

Metrics speak, but only to those who listen with context. A model’s strength is not in numbers alone - but in how we choose to interpret them. #ModelEvaluation #AI #MachineLearning

Plotted the ROC curve, where true and false positives dance across thresholds. The Random Forest reached an AUC of 0.998, near-perfect in discernment. #ROCCurve #AUC #ML

Through the Precision-Recall curve, I saw that increasing precision often means losing recall - a timeless lesson in trade-offs. Balance, in models as in life, is never free. #Precision #Recall #AIInsights

Explored the SGDClassifier and Random Forest Classifier, each revealing a different character in how they measure truth and error. Where SGD learns with steady persistence, Random Forest reasons by consensus. 🌿 #DataScience #ML

The MNIST journey continues, today’s focus: decision thresholds and the quiet dialogue between precision and recall. Every threshold is a choice - between caution and boldness. #MachineLearning #MNIST #AI

Next steps → exploring ROC curves, AUC, and multiclass classification, to deepen model interpretation and performance insight. Every step brings theory closer to intuition. #MachineLearning #AI #DeepLearning #ContinuousLearning

Key insight: Increasing precision often reduces recall, and vice versa. Every classifier must strike its own equilibrium. #MLWorkflow #PerformanceMetrics

Explored precision, recall, and F1 score: Precision: How many predicted positives were correct Recall: How many actual positives were found F1 score: The harmonic mean balancing both sides. #Precision #Recall #F1Score

Introduced confusion matrices - counting how often Class A is mistaken for Class B and vice versa. [[TN, FP], [FN, TP]] It tells stories raw accuracy cannot. #ConfusionMatrix #ModelEvaluation #AI

Built a simple baseline model - Never1Classifier - that never predicts “1”. Surprisingly, it achieves ~91% accuracy. A reminder that high accuracy doesn’t always mean good performance. #MLProject #Python #ScikitLearn

Progress in my MNIST classification project today: shifting focus from accuracy to understanding performance. Learning that evaluating a classifier is an art of balance, not just counting correct predictions. #MachineLearning #DataScience #MNIST

AureusML bridges finance, data science & engineering, paving the way for actionable market insights. Excited to share progress and predictions as the project unfolds! #Innovation #BigData #TechForFinance #FinancialIntelligence

Hyperparameter tuning is crucial: C and gamma values will be optimized for performance, balancing model flexibility and generalization. #MachineLearningWorkflow #SVR #MLEngineering

With the dataset prepared, the next phase is training Support Vector Regressors (SVR) efficiently using RandomizedSearchCV, to identify strong predictive patterns. #Python #SVR #PredictiveAnalytics #QuantFinance

Visual insights: we generated pairplots of opening, closing, and volume. Correlations, trends, and outliers are clearly visible, guiding feature selection and model design. #DataVisualization #Analytics #StockMarket

#AureusML update! Today we advanced the S&P 500 trade data pipeline: long-format dataset, categorical features encoded, numeric features scaled, and training/test sets clearly defined. #MachineLearning #DataScience #Finance

AureusML bridges finance, data science & engineering, laying the groundwork for actionable market insights. Excited to share more as the project unfolds! #Innovation #MLEngineering #BigData #TechForFinance #FinancialIntelligence