https://nirpyresearch.com Statistical learning and chemometrics in Python Sun, 20 Sep 2026 09:08:38 +0000 en-AU hourly 1 https://wordpress.org/?v=7.1.3 https://nirpyresearch.com/wp-content/uploads/2019/07/apple-icon-152x152-1-150x150.png https://nirpyresearch.com 32 32 https://nirpyresearch.com/akaike-information-criterion-for-model-selection/ Sat, 18 Sep 2021 00:56:43 +0000 https://nirpyresearch.com/?p=2552 The Akaike Information Criterion (AIC) is another tool to compare prediction models. AIC combines model accuracy and parsimony in a single metric and can be used to evaluate data processing pipelines or variable selection methods. Here's an introduction.]]> https://nirpyresearch.com/concordance-correlation-coefficient/ Fri, 08 Jan 2021 21:15:00 +0000 https://nirpyresearch.com/?p=2446 The Concordance Correlation Coefficient (CCC) can be useful to quantify the quality of a linear regression model. In this tutorial we explain the CCC and describe its relation with the Pearson's correlation coefficient (r2) and with the Coefficient of Determination (R2).]]> https://nirpyresearch.com/bias-variance-trade-off-pls-regression/ Sun, 20 Sep 2020 06:32:55 +0000 https://nirpyresearch.com/?p=2417 Bias-Variance trade-off refers to the optimal choice of parameters in a model in order to avoid both overfitting and underfitting. Let's look at a worked example using PLS regression.]]> https://nirpyresearch.com/kfold-montecarlo-cross-validation-bootstrap-primer/ Fri, 15 Nov 2019 07:59:11 +0000 https://nirpyresearch.com/?p=2138 Cross-validation is a standard procedure to quantify the robustness of a regression model. Compare K-Fold, Montecarlo and Bootstrap methods and learn some neat trick in the process.]]>