mljar_fit: MLJAR FIT
In mljar/mljar-api-R: R API for MLJAR

Verifies parameters and data and tries to run experiment.

mljar_fit(x, y, validx = NULL, validy = NULL, proj_title = NULL,
  exp_title = NULL, dataset_title = NULL, val_dataset_title = NULL,
  algorithms = c(), metric = "", wait_till_all_done = TRUE,
  validation_kfolds = MLJAR_DEFAULT_FOLDS,
  validation_shuffle = MLJAR_DEFAULT_SHUFFLE,
  validation_stratify = MLJAR_DEFAULT_STRATIFY,
  validation_train_split = MLJAR_DEFAULT_TRAIN_SPLIT,
  tuning_mode = MLJAR_DEFAULT_TUNING_MODE,
  create_ensemble = MLJAR_DEFAULT_ENSEMBLE,
  single_algorithm_time_limit = MLJAR_DEFAULT_TIME_CONSTRAINT)

`x`	data.frame/matrix with training data
`y`	data.frame/matrix with training labels
`validx`	data.frame/matrix with validation data
`validy`	data.frame/matrix with validation labels
`proj_title`	charcater with project title
`exp_title`	charcater with experiment title
`dataset_title`	charcater with dataset name
`val_dataset_title`	charcater with validation dataset name
`algorithms`	list of algorithms to use For binary classification task available algorithm are: "xgb" which is for Xgboost, "lgb" which is for LightGBM "mlp" which is for Neural Network, "rfc" which is for Random Forest, "etc" which is for Extra Trees, "rgfc" which is for Regularized Greedy Forest, "knnc" which is for k-Nearest Neighbors, "logreg" which is for Logistic Regression. For regression task there are available algorithms: "xgbr" which is for Xgboost, "lgbr" which is for LightGBM, "rgfr" which is for Regularized Greedy Forest, "rfr" which is for Random Forest, "etr" which is for Extra Trees.
`metric`	charcater with metric For binary classification there are metrics: "auc" which is for Area Under ROC Curve, "logloss" which is for Logarithmic Loss. For regression tasks: "rmse" which is Root Mean Square Error, "mse" which is for Mean Square Error, "mase" which is for Mean Absolute Error.
`wait_till_all_done`	boolean saying whether function should wait till all models are done
`validation_kfolds`	number of folds to be used in validation
`validation_shuffle`	boolean which specify if shuffle samples before training
`validation_stratify`	boolean which decides whether samples will be divided into folds with the same class distribution
`validation_train_split`	ratio how to split training dataset into train and validation
`tuning_mode`	tuning mode
`create_ensemble`	whether or not to create ensemble
`single_algorithm_time_limit`	numeric with time limit to calculate algorithm