An AI model with 95% accuracy might look impressive—until you realize it missed every single fraud transaction.

In 2025, businesses don’t care about vanity metrics. They care about outcomes: caught frauds, diagnosed diseases, prevented churn.

This is where F1 Score steps in—especially when you’re dealing with imbalanced datasets and real-world consequences.

It balances precision (how many predicted positives are actually correct) with recall (how many actual positives you captured).

In this blog, you’ll learn how the F1 Score is calculated, what makes it “good,” and how to use it to build models that don’t just predict—they perform.

Explore IIT Jodhpur’s B.S/B.Sc in Applied AI & Data Science, a future-facing program designed to prepare you for roles in enterprise AI, from fraud analytics to medical diagnostics.

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What is F1 Score in Machine Learning and Why Does It Matter?

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At its core, the F1 Score is the harmonic mean of precision and recall—two metrics that matter most when accuracy alone can’t tell the full story.

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So, what exactly does that mean?

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In any classification task, your model can make four types of predictions:

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• True Positive (TP): Correctly predicted positive cases
• False Positive (FP): Incorrectly predicted positive cases (a “false alarm”)
• True Negative (TN): Correctly predicted negative cases
• False Negative (FN): Incorrectly predicted negative cases (a “missed case”)

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Now, let’s define the two critical metrics:

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• Precision = TP / (TP + FP)
• → Of all predicted positives, how many were actually positive?
• Recall = TP / (TP + FN)
• → Of all actual positives, how many did the model catch?

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The F1 Score combines these two into a single number:

F1 Score = 2 × (Precision × Recall) / (Precision + Recall)

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Why not just use accuracy?

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Let’s say you’re building a fraud detection model. Only 2 out of 1000 transactions are fraudulent. A model that simply predicts “not fraud” every time will have 99.8% accuracy—and still be useless.

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This is where F1 Score shines. It penalizes models that either:

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• Over-predict positives (low precision)
• Miss actual positives (low recall)

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That makes F1 Score the go-to metric when:

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• You’re working with imbalanced datasets
• False negatives are costly (e.g., missed diagnoses, undetected fraud)
• You want a balanced view of model performance, not just correctness

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In 2025, as ML adoption scales into sectors like insurance underwriting, credit risk scoring, and legal document classification, optimizing for F1 Score ensures that your models aren’t just accurate—they’re actually reliable.

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Also Read: Top 10 AI/Machine Learning Interview Questions (With Expert Answers)

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How to Calculate F1 Score

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The F1 Score isn't just a fancy metric—it's a precise mathematical reflection of how your model balances precision and recall. Let's break down how it works and how to actually calculate it, step by step.

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Step 1: Understand the Building Blocks

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Before you can calculate the F1 Score, you need to compute two key values:

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Precision

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Measures how many of the predicted positive results are actually correct.

Precision=TPTP+FP\text{Precision} = \frac{TP}{TP + FP}

Precision=TP+FPTP

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Recall

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Measures how many of the actual positive cases your model correctly identified.

Recall=TPTP+FN\text{Recall} = \frac{TP}{TP + FN}

Recall=TP+FNTP

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Where:

• TP = True Positives
• FP = False Positives
• FN = False Negatives

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Step 2: Apply the F1 Score Formula

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Once you have precision and recall:

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F1 Score=2×(Precision×RecallPrecision+Recall)\text{F1 Score} = 2 \times \left( \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} \right)

F1 Score=2×(Precision+RecallPrecision×Recall)

This formula uses the harmonic mean, which gives a more conservative average than the arithmetic mean—penalizing extreme imbalances between precision and recall.

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Example: F1 Score in Action

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Let’s say your binary classification model produced the following results on a test set:

• TP = 80
• FP = 20
• FN = 40

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Now calculate:

• Precision = 80 / (80 + 20) = 0.80
• Recall = 80 / (80 + 40) = 0.67

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F1 Score=2×0.80×0.670.80+0.67≈0.73\text{F1 Score} = 2 \times \frac{0.80 \times 0.67}{0.80 + 0.67} ≈ 0.73

F1 Score=2×0.80+0.670.80×0.67≈0.73

So, your model’s F1 Score is 0.73—a solid performance, especially for an imbalanced dataset.

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In programs like IIT Jodhpur’s B.S/B.Sc in Applied AI & Data Science, students don’t just memorize formulas—they calculate F1 Scores across real datasets, optimize classifiers, and learn how metrics like this drive business value.

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Also Read: Top 10 AI/Machine Learning Interview Questions (With Expert Answers)

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How to Calculate F1 Score in Python (Scikit-Learn)

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from sklearn.metrics import f1_score

y_true = [1, 0, 1, 1, 0, 1, 0]
y_pred = [1, 0, 1, 0, 0, 1, 1]

f1 = f1_score(y_true, y_pred)
print("F1 Score:", f1)

Use this in any classification pipeline to monitor your model’s effectiveness beyond accuracy.

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Precision, Recall, and F1 Score: Key Differences

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If you're evaluating a classification model, you’ll often come across precision, recall, and F1 Score—but knowing when to use which metric is what separates a beginner from a machine learning practitioner.

Let’s break it down with clear definitions, examples, and a decision-making framework.

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What is Precision?

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Precision tells you how many of the predicted positives were actually correct.

Precision=TPTP+FP\text{Precision} = \frac{TP}{TP + FP}

Precision=TP+FPTP

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Use Precision When:

• False positives are costly
  • Example: In spam detection, a false positive means sending a legitimate email to the spam folder—annoying for users and potentially damaging.

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What is Recall?

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Recall tells you how many of the actual positives your model caught.

Recall=TPTP+FN\text{Recall} = \frac{TP}{TP + FN}

Recall=TP+FNTP

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Use Recall When:

• False negatives are costly
  • Example: In cancer diagnosis, a false negative means missing a cancer case—a much more serious consequence than a false alarm.

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What is F1 Score?

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The F1 Score balances precision and recall, especially useful when you can’t afford to optimize just one.

F1 Score=2×Precision×RecallPrecision+Recall\text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}

F1 Score=2×Precision+RecallPrecision×Recall

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Use F1 Score When:

• You want a balanced view between false positives and false negatives
• You're working with imbalanced datasets
• Neither precision nor recall alone tells the full story

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Tabular Comparison

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Real-World Example: Fraud Detection

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• Model A is very precise—it rarely calls a non-fraudulent transaction fraudulent—but misses many frauds.
• Model B is more balanced. It finds more fraud without raising too many false alarms.

In practice, Model B would be far more useful—despite lower precision.

That’s why enterprises often optimize for F1 Score, not just precision or recall in isolation.

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F1 Score Range and Interpretation

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The F1 Score always falls between 0 and 1, but what do those numbers actually mean in real-world machine learning?

Let’s decode the full range so you can confidently interpret what makes a "good" or "bad" F1 Score—depending on your use case.

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F1 Score Range: The Basics

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Why You’ll Almost Never See 1.0

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A perfect F1 Score of 1.0 means:

• No false positives (100% precision)
• No false negatives (100% recall)

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Unless your dataset is tiny or artificially perfect, this rarely happens. Most production models score between 0.6 and 0.9, depending on domain complexity.

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F1 Score Thresholds: What's Considered “Good”?

There’s no universal benchmark. A "good" F1 Score is contextual:

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Remember: in imbalanced datasets, even an F1 Score of 0.6 can outperform a 95% accuracy.

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When a Low F1 Score Is Still Useful

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Let’s say your F1 Score is 0.55 in a fraud detection model. Not great on paper, but if:

• You're identifying 10x more fraud cases than before
• Precision is still above industry average
• Business impact is positive

That F1 Score is a win.

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Best Practice:

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Always monitor F1 Score alongside precision and recall to understand why it's high or low—and what you can tweak to improve it.

Also Read: How to Crack a Data Science Interview: 2025 Edition

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What is a Good F1 Score in ML?

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You might’ve seen posts claiming, “A good F1 Score is above 0.80.” But here’s the reality: a “good” F1 Score is not one-size-fits-all—it entirely depends on your domain, your dataset, and your tolerance for risk.

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1. A Good F1 Score is Context-Specific

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Let’s compare two scenarios:

• Medical Diagnosis (e.g., cancer detection):
• A model with F1 Score below 0.85 might be unacceptable—false negatives could mean missed cancer cases.
• Content Tagging on Social Media:
• An F1 Score of 0.60+ might be perfectly fine if speed and scalability matter more than perfection.

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2. Understand Precision-Recall Trade-Offs

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A high F1 Score only means that precision and recall are well-balanced—not necessarily high.

Two models with vastly different precision and recall can still produce the same F1 Score.

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Example:

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• Model A is safer if false positives are costly (e.g., legal alerts).
• Model B is safer if you need overall balance (e.g., support ticket triage).

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3. When You Should Be Cautious of High F1 Scores

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Sometimes, an unusually high F1 Score can indicate:

• Data leakage during training
• Overfitting to test data
• Unrealistically easy test sets

In 2025, with automated ML pipelines and generative data augmentation, these risks are even higher. So don’t just celebrate a 0.95 F1 Score—investigate it.

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4. Benchmarking Matters

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A “good” F1 Score is also relative to:

• Baseline models (e.g., majority class classifier)
• Previous models in production
• Industry standards (if available)

Tools like MLflow, Weights & Biases, or your enterprise MLOps dashboard can help you track this over time.

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Key Takeaway

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A good F1 Score is not a magic number—it’s a metric that tells you whether your model is making the right trade-offs.

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If it aligns with business goals, regulatory thresholds, or user experience requirements, then that’s a good F1 Score.

Understanding how to benchmark and interpret F1 Scores isn’t just an academic skill—it’s what top employers expect from applied AI professionals.

That’s why programs like IIT Jodhpur’s B.S/B.Sc in Applied AI & Data Science

bake industry-aligned evaluation techniques directly into their curriculum.

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How to Improve F1 Score in Machine Learning

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If your model’s F1 Score is underperforming, don’t panic. Improving it isn’t just about tweaking formulas—it’s about understanding where your model is failing and fixing it strategically.

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Here’s how you can start.

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1. Handle Class Imbalance First

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F1 Score is extremely sensitive to class imbalance. If one class dominates the dataset, your model might ignore the minority class entirely.

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Solutions:

• Use SMOTE (Synthetic Minority Oversampling)
• Try undersampling the majority class
• Apply class weights during model training

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2. Tune the Decision Threshold

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Most classification models use a default threshold of 0.5—but that’s arbitrary. You can shift the threshold to favor precision or recall, depending on your needs.

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python
CopyEdit
from sklearn.metrics import f1_score

thresholds = [0.3, 0.5, 0.7]
for t in thresholds:
    y_pred = (model.predict_proba(X_test)[:,1] > t).astype(int)
    print(f"Threshold {t} - F1 Score: {f1_score(y_test, y_pred)}")

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3. Try Better Algorithms

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Some models simply perform better with imbalanced data.

• Use tree-based models like Random Forest or XGBoost
• Consider ensemble methods or gradient boosting
• For neural networks, monitor precision/recall per epoch and apply early stopping

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4. Improve Data Quality

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Garbage in, garbage out. If your features are weak or mislabeled, your F1 Score will reflect that.

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What you can do:

• Conduct feature selection and feature engineering
• Audit label quality
• Add domain-specific features that enhance model discrimination

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5. Evaluate F1 Score Per Class

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Especially in multi-class problems, your macro-average F1 Score might hide poor class-level performance.

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python
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from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

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Use this to identify which classes need targeted improvement.

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6. Learn to Optimize with Industry Context

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F1 Score isn't just a number—it’s tied to how your model behaves in production.

That’s why students in IIT Jodhpur’s B.S/B.Sc in Applied AI & Data Science are trained to tune models not for Kaggle wins, but for real-world outcomes like fraud reduction, medical accuracy, and operational efficiency.

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Also Read: AI vs Machine Learning vs Data Science – What’s the Difference and Which Should You Learn?

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F1 Score vs Accuracy vs AUC: What’s the Difference and When to Use Each

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Choosing the right evaluation metric can make or break your machine learning project. While F1 Score, accuracy, and AUC-ROC are often used interchangeably, they measure very different things—and using the wrong one can lead to misleading conclusions.

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Let’s break them down.

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1. Accuracy: The Most Misleading Metric (Sometimes)

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Accuracy=TP+TNTP+TN+FP+FN\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}

Accuracy=TP+TN+FP+FNTP+TN

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Use accuracy when:

• Your dataset is balanced
• Both false positives and false negatives have similar consequences

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Avoid it when:

• You have imbalanced data
• Missing rare events (like fraud or disease) is risky

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Example: In a dataset with 99% non-fraud cases, predicting “non-fraud” every time gives you 99% accuracy—but zero value.

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2. F1 Score: Precision + Recall’s Lovechild

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F1 Score=2×Precision×RecallPrecision+Recall\text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}

F1 Score=2×Precision+RecallPrecision×Recall

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Use F1 Score when:

• You need a balance between false positives and false negatives
• The dataset is imbalanced
• You’re working in high-stakes domains like healthcare, finance, or legal tech

It’s the go-to metric for teams optimizing outcomes, not just optics.

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3. AUC-ROC: Ranking Model Confidence

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AUC-ROC evaluates how well your model ranks predictions—not just labels them.

• ROC Curve: Plots True Positive Rate vs False Positive Rate at various thresholds
• AUC (Area Under Curve): The total area under the ROC curve, from 0 to 1

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Use AUC when:

• You want to evaluate the model’s probability output
• Thresholds will be set later in deployment
• You're comparing multiple models' general discriminative ability

An AUC of 0.90 means the model ranks a random positive instance higher than a random negative one 90% of the time.

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Quick Comparison Table

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‍Which One Should You Use?

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If you’re serious about mastering these metrics—and understanding when to trust what—the IIT Jodhpur B.S/B.Sc in Applied AI & Data Science builds this judgment into your training through real-world model deployments and iterative performance evaluation.

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tl;dr

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• F1 Score = Harmonic mean of precision and recall
• Ideal for imbalanced datasets where accuracy misleads
• Formula:F1=2×Precision+RecallPrecision×Recall
• F1=2×Precision×RecallPrecision+RecallF1 = 2 \times \frac{Precision \times Recall}{Precision + Recall}
• Good F1 Score depends on context—0.80+ in healthcare, 0.60+ in NLP tasks
• Improve F1 by tuning thresholds, handling class imbalance, and enhancing feature quality
• Use F1 over accuracy when false positives and false negatives both matter
• AUC-ROC evaluates model ranking; F1 measures labeling performance
• Programs like IIT Jodhpur’s B.S/B.Sc in Applied AI & Data Science embed these skills into job-ready learning

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