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--- clase:iabd:pia:documentacion [2024/05/09 12:32]
admin [Intervalos de confianza]
+++ clase:iabd:pia:documentacion [2024/12/14 20:39] (actual)
admin [Pipelines , MLOps]
@@ Línea 127: / Línea 127: @@
   * [[https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962|Probability Learning II: How Bayes’ Theorem is applied in Machine Learning]]
   * {{ :clase:iabd:pia:a_gentle_introduction_to_bayesian_analysis.applications_to_development_research.pdf|A Gentle Introduction to Bayesian Analysis.Applications to Development Research}}
+  * [[https://en.wikipedia.org/wiki/Pre-_and_post-test_probability|Pre- and post-test probability]]
@@ Línea 148: / Línea 148: @@
   * [[https://medium.com/analytics-vidhya/colab-vs-code-github-jupyter-perfect-for-deep-learning-2b257ae94d01|Colab + Vs Code + GitHub + Jupyter (Perfect for Deep Learning)]]
   * [[https://www.tensorflow.org/guide/keras/save_and_serialize|Guardando y Serializando Modelos con TensorFlow Keras]]
+  * [[https://365datascience.com/tutorials/machine-learning-tutorials/how-to-deploy-machine-learning-models-with-python-and-streamlit/|How to Deploy Machine Learning Models with Python & Streamlit]]
@@ Línea 405: / Línea 406: @@
     * {{ :clase:iabd:pia:2eval:riesgo_de_contagio_por_covid-19_en_funcion_del_tipo_de_contacto_y_de_la_renta_familiar.pdf |Riesgo de contagio por COVID-19 en función del tipo de contacto y de la renta familiar}}
     * {{ :clase:iabd:pia:2eval:prevalencia_de_infeccion_por_coronavirus_sars-cov-2_en_pacientes_y_profesionales_de_un_hospital_de_media_y_larga_estancia_en_espana.pdf |Prevalencia de infección por coronavirus SARS-CoV-2 en pacientes y profesionales de un hospital de media y larga estancia en España}}
+===== Probabilidad del resultado de la red =====
+  * {{ :clase:iabd:pia:2eval:introduction_to_uncertainty_in_deep_learning.pdf |Introduction to Uncertainty in Deep Learning}}
+  * {{ :clase:iabd:pia:2eval:calibrar_modelos_de_machine_learning.pdf |Calibrar modelos de machine learning}}
+  * [[https://wttech.blog/blog/2021/a-guide-to-model-calibration/|A guide to model calibration]]
+  * {{ :clase:iabd:pia:2eval:predicting_good_probabilities_with_supervised_learning.pdf |Predicting Good Probabilities With Supervised Learning}}
+  * {{ :clase:iabd:pia:2eval:neural_network_prediction_scores_are_not_probabilities.pdf |Neural Network Prediction Scores are not Probabilities}}
+  * [[https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/|How and When to Use a Calibrated Classification Model with scikit-learn]]
+  * scikit-learn
+    * [[https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html|Probability Calibration curves]]
+    * [[https://scikit-learn.org/stable/modules/generated/sklearn.calibration.calibration_curve.html|sklearn.calibration.calibration_curve]]
+    * [[https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html|sklearn.calibration.CalibratedClassifierCV]]
+  * Regresión logística isotónica
+  * Calibración de Platt
+===== PyMC3 =====
+  * {{ :clase:iabd:pia:2eval:bayesian_linear_regression_in_python_using_machine_learning_to_predict_student_grades_part_2.pdf |Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2}}
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