2022_ML_PHW1

Machine learning subject PHW1 assignment results for the second semester of 2022

Program description for PHW 1 programming task.

The dataset used in this function is in the form of a Python pandas data frame.

def make_model(K,data,case,**kwargs):

args : K = K_value for Kfold , data = pandas.dataframe for training, case = input switch for model selection, kwargs = args for classification model
- According to the case argument input, one of decision tree, logistic, and svm classification models are created and the classification_compare function is executed based on the generated model.
- Users can input model parameters for model training in a Python dictionary way(**kwargs)
- Returns the return value(Model Accuracy Score array) of the classification_Compare function.

def classification_Compare(data,model,K):

Args: data = pandas.dataframe for training, mode = classification model , K = K_value for Kfold
- It trains the training data on the model input as a factor, validates the trained model, and returns the model's accuracy score.
- The training data input as a factor is classified into training and validation datasets through the K-fold method. (Factor K is used as input for this K-fold.)
- There is also an additional model validation process for the dataset that has been preprocessed through the min_max scale for the training data.
- Returns an array of model accuracy scores for each training and validation dataset generated in a K-fold.