Machine Learning Accuracy: True-False Positive/Negative [2023]

ByPaul Christiano Last Update on

Hi there! As a data analyst specializing in machine learning, model accuracy is a topic near and dear to my heart. Evaluating how often machine learning models make correct predictions vs mistakes is so important – but it can also be deceptively tricky. In this guide, I‘ll walk you through the key accuracy metrics you need to know, like true positives and false positives, using simple real-world examples. I‘ll also share insider tips on how to improve model accuracy based on my experience. Let‘s get started!

What Exactly is Model Accuracy?

Accuracy refers to how often a machine learning model generates the right prediction. For example, say we have a model classifying emails as either spam or not spam. If the model correctly labels 90 out of 100 emails, its accuracy is 90%.

The accuracy calculation is straightforward:

Accuracy = (Number of correct predictions) / (Total number of predictions)

On the surface, high accuracy sounds great. But there‘s more to the story, as we‘ll see next.

Why True Positives and False Positives Matter

Here‘s the key insight about accuracy: all incorrect predictions are not equal. To properly evaluate a model, we need to distinguish between its errors, which fall into two main categories:

False Positives: The model incorrectly predicts the positive class
False Negatives: The model incorrectly predicts the negative class

Let‘s say we have a model predicting whether a patient has a medical condition.

  • Predicting they have it when they don‘t is a false positive
  • Predicting they don‘t have it when they actually do is a false negative

Clearly, these two kinds of errors have very different impacts!

When assessing accuracy, we need to look beyond overall accuracy and consider the business costs of different mistake types.

True Positive Rate (TPR) and False Positive Rate (FPR)

Two metrics that provide these insights are:

  • True Positive Rate (TPR): % of actual positives correctly predicted
  • False Positive Rate (FPR): % of actual negatives incorrectly predicted as positive

Let‘s walk through an example:

Our medical test accurately detects the condition in 90 out of 100 sick patients, but incorrectly flags it in 40 out of 1,000 healthy patients.

  • True Positive Rate = 90/100 = 90%
  • False Positive Rate = 40/1,000 = 4%

For this test, the TPR is excellent but the FPR may be too high, causing many unnecessary procedures and patient stress.

Balancing these rates is vital based on the business context. For fraud detection, we want very high TPR, even if it increases FPR. But for medical tests, we likely want FPR ≤ 1% to minimize false alarms.

How Predictive Value Helps Balance TPR and FPR

In addition to TPR and FPR, we also want to know:

  • Positive Predictive Value (PPV): When the model predicts positive, how often is it right?
  • Negative Predictive Value (NPV): When the model predicts negative, how often is it right?

High PPV means a positive prediction can be trusted. High NPV means a negative prediction is likely true.

Let‘s walk through an example:

Out of 1,000 loan applicants, our credit risk model flags 100 as high-risk, of which 80 default. It passes 900 applicants, of which 860 repay the loan.

  • Positive Predictive Value = 80/100 = 80%
  • Negative Predictive Value = 860/900 = 96%

Here, PPV is only 80% – so 20% of flagged applicants are wrongly rejected. But NPV is 96% – nearly all approved applicants repay the loan.

How to Balance Business Needs with Model Accuracy

Let‘s pull this all together: evaluating model accuracy requires balancing multiple metrics based on business needs:

  • High TPR is crucial when false negatives have high cost (e.g. medical diagnoses, fraud detection)
  • Low FPR is crucial when false positives have high cost (e.g. email spam detection)
  • High PPV is needed when positive predictions drive important business decisions
  • High NPV is needed when negative predictions enable business decisions

As a simple example, let‘s look at a model predicting customer churn. Here‘s a sample confusion matrix:

  • Accuracy = 85%
  • True Positive Rate = 70%
  • False Positive Rate = 15%
  • Positive Predictive Value = 80%
  • Negative Predictive Value = 90%

For this application, I would be concerned about the 70% True Positive Rate – we want to correctly identify as many customers at risk of churning as possible, even if it increases false positives.

The 80% Positive Predictive Value indicates we can trust positive predictions and intervene to retain those customers. And the 90% Negative Predictive Value means customers predicted as not at risk are likely safe to ignore.

Based on this analysis, I would likely retrain the model to improve TPR further, even if it meant lower overall accuracy. Successfully identifying customers about to churn has a huge business benefit and is worth added false positives.

Tips to Improve Model Accuracy from a Machine Learning Expert

Here are a few techniques I use regularly to improve model accuracy:

Fine tune hyperparameters: Carefully tuning model hyperparameters like neural network structure, learning rate, and regularization strength can boost accuracy.

Use ensemble models: Combining multiple models together often improves predictions. For example, averaging the output of 5 neural networks may be more robust.

Handle class imbalance: If positives and negatives are imbalanced, resampling or weighting data points helps accuracy.

Add informative features: Pulling in additional useful data variables for the model to consider helps it make better predictions.

Prevent overfitting: Regularization, cross-validation and dropout help prevent over-optimizing on training data which hurts accuracy on real-world data.

Update training data: Periodically retraining models on fresh data maintains accuracy as data patterns change over time.

Remove bias: Accuracy issues can result from biases in data. Ensuring training data is representative and filtering biased variables helps.

There‘s no silver bullet – getting high "real world" accuracy requires both model tuning expertise and understanding business needs. But focus on the metrics that matter most and you‘ll be on the right track.

I hope this overview was useful in demystifying model accuracy! Feel free to reach out if you have any other machine learning questions.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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