Classification methods applied to an imbalanced big dataset

Here you can find my firstMachine Learning project in R:

✅ R_markdown

✅ RPubs

⭐ Description:

This analysis is part of the final project of the subject Machine Learning 1: classification methods taught at the University of Warsaw. The final project consisted in two projects regression and classification made together with Lashari Gochiashvili.

The purpose of this analysis is to apply classification methods to a big dataset, in order to classify the cars with a symboling security level: secure, neutral and risky.

In this analysis several methods will be applied, such as learning vector quantization, multinomial regression, penalized multinomial regression, k-nearest neighbors, support bvector machine, linear discriminant analysis and quadratic discriminant analysis.

Aditionaly, several techniches will be applied in order to find the best model performance, among which we can find down-sampling, cross-validation, tuning our model by different parameters and pre processing.

All of these techniques are based on the caret package, one of the most known R package for Machine Learning

⭐ Some insights:

☑️ 1) Imabalanced data:

☑️ 2) Categorical variables:

☑️ 3) Numeric variables:

☑️ 4) Learning Vector Quantization:

☑️ 5) k-nearest Neighbors:

☑️ 6) Results:

⭐ Conclusions:

📍 For the dataset cars the best classification model is Quadratic Discriminant with repeated cross validation and down-sampling

📍 In imbalanced data one cannot trust the accuracy to compare models, the model tends to predict the most common class, hence other kind of measures are recomended, in our case ROC was used

📍 K nearest neighbors and support vector machine are models that do not work very well with multiclass dependent variable, it would be better to use them in case of binomial depdent varaible

📍 Before applying demanding models, it is convenient to analize ceteris paribus what is expected to happen, in our case it was analized what will happen when down-sampling is applied

📍 Computational time matters, and it should be taken into consideration when applying machine learning models, hence the data preparation is really important in these cases. In certain instances it is recommended to parallel the process, when the algorithm allows to do it (doParallel and doMC packages can be used)

📍 In case of having a big data and imbalance clases it is really productive to use down-sampling, in terms of computational time and performance of the model

📍 When performing a Machine Learning analysis it is important to have enough memory in the system which would allow one to save the results