ClassifyAnything

ClassifyAnything is a flexible tool designed to help you perform supervised classification on any type of 2D matrix data using Jupyter Notebooks. The main goals of this tool are:

1. To offer all the needed methods in each step for a hands-on experience.
2. To explain what's going on, so you can understand the process.
3. To help you pick the best model for your data.

How to use this pipeline?

1. Clone this project into your local project folder.
2. Work on the Jupyter notebooks in order according to your purpose.
3. Follow the instruction and explanation step by step for exploring your data.
4. Wrap all the analyses into a HTML report by running bash render_reports.sh.

Configure the environment

conda env create -n ClassifyAnything -f conda_environment.yml

Supervised Learning Workflow

1. Data Loading and Preprocessing

01_Preprocessing.ipynb - Load the dataset into your Jupyter notebook - Explore the dataset - Perform necessary data preprocessing steps, such as: - Handling missing values - Handling outliers - Feature scaling or normalization

2. Feature Selection

02_Feature_selection.ipynb - Removing features with low variance - Univariate feature selection

3. Model Selection and Training

03_Model_selection.ipynb - Split the dataset into training and test sets - Training dataset will be used for fine-tuning the hyperparameters of each model with k-fold validation. - Test dataset will be used at the end for benchmarking different models.

3-1. Logistic Regression:

03-01_Logistic_Regression.ipynb

• Logistic regression models the relationship between input features and the probability of belonging to a particular class.
• It is a linear model that uses a logistic function to map the output to a probability value.
• Logistic regression is often used for binary classification tasks, but can be extended to multi-class classification as well.

3-2. Decision Trees:

03-02_Decision_Trees.ipynb

• Decision trees partition the feature space into regions based on feature values and make predictions based on the majority class in each region.
• They are intuitive and easy to interpret, as they represent a flowchart-like structure of if-else decision rules.

3-3. Random Forests:

03-03_Random_Forest.ipynb

• Random forests are an ensemble learning method that combines multiple decision trees to make predictions.
• They create a set of decision trees using random subsets of features and average the predictions from individual trees to obtain the final prediction.
• Random forests are known for their robustness and ability to handle high-dimensional data.

3-4. Support Vector Machines (SVM):

03-04_SVM.ipynb

• SVMs find an optimal hyperplane that separates different classes by maximizing the margin between them.
• They can handle linear and non-linear classification tasks by using different kernel functions.
• SVMs work well with both small and large datasets and are effective in high-dimensional spaces.

3-5. Naive Bayes:

• Naive Bayes classifiers are based on Bayes' theorem and assume that features are conditionally independent given the class label.
• They are computationally efficient and can handle large datasets.
• Naive Bayes classifiers work well in situations where the independence assumption holds reasonably well.

3-6. K-Nearest Neighbors (KNN):

• KNN is a non-parametric algorithm that classifies new instances based on the class labels of k nearest neighbors in the training set.
• It does not explicitly learn a model and relies on the proximity of instances in the feature space.
• KNN can handle multi-class classification and can work well when the decision boundary is non-linear.

3-7. Gradient Boosting methods:

• Gradient Boosting methods, such as XGBoost and LightGBM, build an ensemble of weak prediction models in a sequential manner.
• Each subsequent model corrects the errors made by the previous models, leading to improved performance.
• Gradient Boosting methods often provide excellent predictive accuracy but require careful hyperparameter tuning.

These are just a few examples of supervised learning algorithms for classification tasks. The choice of algorithm depends on various factors such as the nature of the data, the size of the dataset, interpretability requirements, and performance goals. It's often useful to experiment with different algorithms and compare their performance to determine the most suitable one for your specific classification problem.

4. Model Evaluation

- Evaluate the trained model on the test set
- Calculate and interpret the performance metrics relevant to your task, such as: Accuracy, Precision, Recall, F1-score.
- Discuss the implications of the evaluation metrics in assessing the model's performance