🔹Using LIME to Understand a Machine Learning Model’s #Predictions
Using a record explainer mechanism like Local Interpretable #Model_Agnostic Explanations (LIME) is an important technique to filter through the predicted outcomes from any machine learning model. This technique is powerful and fair because it focuses more on the inputs and outputs from the model, rather than on the model itself.
#LIME works by making small tweaks to the input #data and then observing the impact on the output data. By #filtering through the model’s findings and delivering a more digestible explanation, humans can better gauge which predictions to trust and which will be the most valuable for the organization.
———————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#machinelearning
#datascience
#deeplearning
#AI
Using a record explainer mechanism like Local Interpretable #Model_Agnostic Explanations (LIME) is an important technique to filter through the predicted outcomes from any machine learning model. This technique is powerful and fair because it focuses more on the inputs and outputs from the model, rather than on the model itself.
#LIME works by making small tweaks to the input #data and then observing the impact on the output data. By #filtering through the model’s findings and delivering a more digestible explanation, humans can better gauge which predictions to trust and which will be the most valuable for the organization.
———————————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.rocketsource.co/blog/machine-learning-models/
#machinelearning
#datascience
#deeplearning
#AI