Econometrıcs II (ENG) — Ünite 8 Soru-Cevap

Regression models are referred to as inherently interpretable models because their prediction values, created through the equations they provide, are understandable.

The AI Act is a proposed legislation by the European Commission aimed at regulating the ethical and trustworthy use of artificial intelligence systems across the European Union, addressing potential risks and ensuring fundamental rights.

Two fundamental paradigms within machine learning are supervised learning and unsupervised learning, each offering unique approaches to extracting knowledge from data.

In supervised learning, the model is trained on a labelled dataset, where each independent variable is associated with a corresponding response variable.

Regression tasks involve predicting a continuous numerical output or response variable based on input features.

The primary goal is to learn a mapping from independent variables to discrete output labels and make accurate predictions about the class of a given instance.

In unsupervised learning, the model works with unlabeled data, which is similar to exploring patterns in economic data without a pre-defined response variable.

Supervised learning in econometrics involves training models to predict economic response variables based on historical data, while unsupervised learning finds hidden structures or patterns within economic datasets without predefined labels.

They are interpretable, effective in capturing non-linear relationships, inherently highlight variable importance and tend to overfit.

Logistic regression is a classification algorithm, not a regression algorithm. It is well-suited for scenarios where the response variable is binary, meaning it has only two classes. Logistic regression models extend the principles of linear regression by applying the logistic function to the linear combination of input features. The logistic function, also known as the sigmoid function, transforms the output into a range between 0 and 1. The model predicts the probability that an instance belongs to the positive class.

Hyperparameters are external configuration settings for a model that are not learned from the data but are set prior to the training process.

Random forests algorithm has gained prominence for its robust performance in various domains. It belongs to the ensemble learning family, combining the strengths of multiple decision trees to deliver powerful and accurate predictions

They are characterized by high accuracy and robustness, their resistance to overfitting is a key strength, rendering random forests resilient to noise in the data and finally there is a computational cost involved.

These measures provide insights into the model’s accuracy, reliability, and ability to generalize to new, unseen data.

The black-box machine learning models can not be inherently interpretable while the glassbox machine learning models can inherently interpretable.

Shapley additive explanations can be used as an alternative method to tackle the problem of ordering in a Break-Down plot.

The LIME method aims to explain the contribution of the variables on a complex black-box model for a specific observation.

Ceteris-paribus (CP) profiles explain the impact on a model’s prediction when the value of a single independent variable is changed.

This perspective is particularly valuable when considering the model’s predictions as a representation of a larger population. 

The partial-dependence profiles, which are the average of a collection of individual ceteris-paribus profiles, can be employed to show the behavior of the expected value of model predictions concerning an intended independent variable.