[DEFAULT]
class_map = {}
custom_learner_path = 
cv_folds_file = 
folds_file = 
feature_hasher = False
feature_scaling = none
featuresets = []
featureset_names = []
fixed_parameters = []
grid_search = False
grid_search_folds = 3
grid_search_jobs = 0
hasher_features = 0
id_col = id
ids_to_floats = False
label_col = y
log = 
learning_curve_cv_folds_list = []
learning_curve_train_sizes = []
min_feature_count = 1
models = 
num_cv_folds = 10
metrics = []
objectives = ['f1_score_micro']
objective = f1_score_micro
param_grids = []
pos_label_str = 
predictions = 
probability = False
random_folds = False
results = 
sampler = 
sampler_parameters = []
save_cv_folds = False
shuffle = False
suffix = 
test_directory = 
test_file = 
train_directory = 
train_file = 
use_folds_file_for_grid_search = True

[General]
experiment_name = auto_dir_creation
task = evaluate

[Input]
learners = ['LogisticRegression']
suffix = .jsonlines
custom_learner_path = ../other/majority_class_learner.py
train_file = ../train/f0.jsonlines
test_file = ../train/f1.jsonlines

[Tuning]
grid_search = true
objectives = ['accuracy']
objective = f1_score_micro

[Output]
probability = false
results = /Users/nmadnani/work/skll/tests/autoresults
log = /Users/nmadnani/work/skll/tests/autolog
models = /Users/nmadnani/work/skll/tests/automodels
predictions = /Users/nmadnani/work/skll/tests/autopredictions

