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 courseCPMAI Training and CertificationAI GlossaryGlossary Series: Training Data, Epoch, Batch, Learning CurveGlossary Series: (Artificial) Neural Networks, Node (Neuron), LayerGlossary Series: Bias, Weight, Activation Function, Convergence, ReLUGlossary Series: PerceptronGlossary Series: Hidden Layer, Deep LearningGlossary Series: Loss Function, Cost Function & Gradient DescentGlossary Series: Backpropagation, Learning Rate, OptimizerGlossary Series: Feed-Forward Neural NetworkGlossary Series: OpenAI, GPT, DALL-E, Stable DiffusionGlossary Series: Natural Language Processing (NLP), NLU, NLG, Speech-to-Text, TTS, Speech RecognitionAI Glossary Series – Machine Learning, Algorithm, ModelAI Glossary Series – Model Tuning and HyperparameterAI Glossary Series: Overfitting, Underfitting, Bias, Variance, Bias/Variance TradeoffGlossary Series: Classification & Classifier, Binary Classifier, Multiclass Classifier, Decision BoundaryContinue reading AI Today Podcast: AI Glossary Series – Confusion Matrix, Accuracy, Precision, F1, Recall, Sensitivity, Specificity, Receiver-Operating Characteristic (ROC) Curve at Cognilytica.

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