robindehouck/ml_random_forest

Here is the template I created to train quickly a powerful new Machine Learning model.

Random Forest Model

This repository contains a script that trains and evaluates a random forest model using the sklearn library in Python. The model can be used for either classification or regression tasks, depending on the type of target variable.

Dependencies Python 3.7 or higher pandas scikit-learn joblib Usage To use the script, you will need to provide a path to a clean dataset in the form of a csv file. The dataset should not contain any missing values, and all categorical variables should be encoded as numeric values.

The script will automatically split the dataset into training and testing sets, and will fit the model using the training data. The model's performance will then be evaluated on the testing data.

The model can be saved for future use by using the joblib.dump() function.

Potential Improvements

There are a number of ways to improve the model's performance, including:

Tuning the model's hyperparameters Adding additional features to the training data Using a different type of model altogether (e.g. decision tree, gradient boosting) Using ensemble methods to combine the predictions of multiple models

Data Preprocessing

Before fitting the model, the script performs a few preprocessing steps on the dataset:

It selects all categorical variables and encodes them using the LabelEncoder class from sklearn.preprocessing. This ensures that all categorical variables are in a numeric form that the model can process.

It splits the dataset into training and testing sets using the train_test_split() function from sklearn.model_selection. This allows the model to be trained and evaluated on separate data, which is a best practice in machine learning.

Model Training and Evaluation

The model is trained using the fit() method of the RandomForestClassifier or RandomForestRegressor class (depending on the type of target variable). The model is then used to make predictions on the testing data using the predict() method.

Finally, the script calculates the accuracy of the model's predictions using the accuracy_score() function from sklearn.metrics. This metric provides a simple way to measure the model's performance, although other evaluation metrics (such as precision, recall, and F1 score) may be more appropriate depending on the specifics of the problem.

Here is an example of the output that you might see when running the script:

Accuracy: 0.88
              actual      pred
0         0.000000  0.019231
1         0.333333  0.353846
2         0.666667  0.653846
3         1.000000  0.980769
4         0.666667  0.653846

The first column shows the actual values of the target variable in the testing set, while the second column shows the model's predictions. The final line shows the overall accuracy of the model's predictions.