AI Today Podcast: AI Glossary Series – Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve
In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve, explain how these terms relate to AI and why it's important to know about them. Show Notes: FREE Intro to CPMAI mini course CPMAI Training and Certification AI Glossary Glossary Series: Training Data, Epoch, Batch, Learning Curve Glossary Series: (Artificial) Neural Networks, Node (Neuron), Layer Glossary Series: Bias, Weight, Activation Function, Convergence, ReLU Glossary Series: Perceptron Glossary Series: Hidden Layer, Deep Learning Glossary Series: Loss Function, Cost Function & Gradient Descent Glossary Series: Backpropagation, Learning Rate, Optimizer Glossary Series: Feed-Forward Neural Network Glossary Series: OpenAI, GPT, DALL-E, Stable Diffusion Glossary Series: Natural Language Processing (NLP), NLU, NLG, Speech-to-Text, TTS, Speech Recognition AI Glossary Series - Machine Learning, Algorithm, Model AI Glossary Series - Model Tuning and Hyperparameter AI Glossary Series: Overfitting, Underfitting, Bias, Variance, Bias/Variance Tradeoff Glossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision Boundary