From ffb7cbdae1d3a1437744f5992d820dc956d22a38 Mon Sep 17 00:00:00 2001
From: igraf <igraf@cl.uni-heidelberg.de>
Date: Fri, 23 Feb 2024 21:55:01 +0100
Subject: [PATCH] Update README
---
project/README.md | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/project/README.md b/project/README.md
index 5f930d5..3d7feb5 100644
--- a/project/README.md
+++ b/project/README.md
@@ -304,6 +304,8 @@ We have conducted a series of experiments to evaluate the performance of differe
🔠For our basic classifiers (Naive Bayes, Decision Tree, Random Forest), we have utilized scikit-learn's `GridSearchCV` to optimize the hyperparameters. This approach allows us to systematically test different hyperparameter combinations and identify the best configuration for each classifier. In order to use the development set for hyperparameter tuning, we have used the `PredefinedSplit` function to create a fixed split between the training and development set instead of using a default cross-validation split.
+For the random forest classifier, we have conducted additional experiments involving various picture sizes and number of estimators. This exploration was motivated by the notably promising results obtained from this classifier compared to the other basic classifiers.
+
â†ªï¸ To reproduce the results, please refer to the [README](src/README.md) in the `src` folder for instructions on how to run the experiments.
### Feature Engineering
@@ -422,8 +424,8 @@ The following table shows an excerpt of the feature and size combination used:
| Resized | Features | Accuracy (Dev) | Best Parameters | Comments |
| ------- | -------- | -------- | --------------- | ---- |
| 50x50 | No filters (7500 Features) | 0.417 | `{'max_depth': 70, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}` |
-| 50x50 | HSV only | 0.469 | `{'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}`||
-| 50x50 | HSV + Sobel | 0.469 | `{'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}` | => no improvement compared to only HSV
+| 50x50 | HSV only | **0.469** :thumbsup: | `{'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}`||
+| 50x50 | HSV + Sobel | **0.469** :thumbsup: | `{'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}` | => no improvement compared to only HSV
| 50x50 | Sobel only | 0.392 | `{'max_depth': 40, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}` | => a lot worse than HSV only
| 50x50 | No filters + Sobel | 0.432 | `{'max_depth': 30, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 100}` | |
| 50x50 | Canny only | 0.203 | `{'max_depth': 70, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}` | => poor results
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