Experiment Name: test_int_labels_cv
SKLL Version: 1.3
Training Set: test_int_labels_cv.jsonlines
Training Set Size: 27
Test Set: cv
Test Set Size: n/a
Shuffle: False
Feature Set: ["train_test_int_labels_cv.jsonlines"]
Learner: Ridge
Task: cross_validate
Number of Folds: 10
Stratified Folds: True
Feature Scaling: none
Grid Search: False
Scikit-learn Version: 0.19.1
Start Timestamp: 13 Nov 2017 16:20:27.390210
End Timestamp: 13 Nov 2017 16:20:27.433158
Total Time: 0:00:00.042948


Fold: 1
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  2.0000 (actual),  1.3961 (predicted)
 Max =  3.0000 (actual),  2.6856 (predicted)
 Avg =  2.3333 (actual),  2.2139 (predicted)
 Std =  0.4714 (actual),  0.5805 (predicted)
Pearson =  0.421525
Objective Function Score (Test) = nan

Fold: 2
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  2.0000 (actual),  2.7437 (predicted)
 Max =  5.0000 (actual),  4.7409 (predicted)
 Avg =  3.6667 (actual),  3.6819 (predicted)
 Std =  1.2472 (actual),  0.8198 (predicted)
Pearson =  0.956962
Objective Function Score (Test) = nan

Fold: 3
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  1.0000 (actual),  2.2453 (predicted)
 Max =  3.0000 (actual),  3.1726 (predicted)
 Avg =  2.0000 (actual),  2.7180 (predicted)
 Std =  0.8165 (actual),  0.3788 (predicted)
Pearson =  0.528795
Objective Function Score (Test) = nan

Fold: 4
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  2.0000 (actual),  1.3895 (predicted)
 Max =  3.0000 (actual),  2.6876 (predicted)
 Avg =  2.3333 (actual),  2.2115 (predicted)
 Std =  0.4714 (actual),  0.5837 (predicted)
Pearson =  0.419062
Objective Function Score (Test) = nan

Fold: 5
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  2.0000 (actual),  2.7179 (predicted)
 Max =  5.0000 (actual),  4.7316 (predicted)
 Avg =  3.6667 (actual),  3.6733 (predicted)
 Std =  1.2472 (actual),  0.8253 (predicted)
Pearson =  0.961492
Objective Function Score (Test) = nan

Fold: 6
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  1.0000 (actual),  2.2341 (predicted)
 Max =  3.0000 (actual),  3.1528 (predicted)
 Avg =  2.0000 (actual),  2.7076 (predicted)
 Std =  0.8165 (actual),  0.3756 (predicted)
Pearson =  0.545195
Objective Function Score (Test) = nan

Fold: 7
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  2.0000 (actual),  1.3783 (predicted)
 Max =  3.0000 (actual),  2.6947 (predicted)
 Avg =  2.3333 (actual),  2.2055 (predicted)
 Std =  0.4714 (actual),  0.5882 (predicted)
Pearson =  0.406418
Objective Function Score (Test) = nan

Fold: 8
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  4.0000 (actual),  3.5340 (predicted)
 Max =  5.0000 (actual),  4.7077 (predicted)
 Avg =  4.5000 (actual),  4.1209 (predicted)
 Std =  0.5000 (actual),  0.5869 (predicted)
Pearson =  1.000000
Objective Function Score (Test) = nan

Fold: 9
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  2.0000 (actual),  2.7474 (predicted)
 Max =  3.0000 (actual),  2.7683 (predicted)
 Avg =  2.5000 (actual),  2.7578 (predicted)
 Std =  0.5000 (actual),  0.0104 (predicted)
Pearson = -1.000000
Objective Function Score (Test) = nan

Fold: 10
Model Parameters: {"normalize": false, "fit_intercept": true, "max_iter": null, "random_state": 123456789, "tol": 0.001, "copy_X": true, "alpha": 1.0, "solver": "auto"}
Grid Objective Score (Train) = 0.0

Accuracy = 
Descriptive statistics:
 Min =  1.0000 (actual),  2.2573 (predicted)
 Max =  2.0000 (actual),  3.1685 (predicted)
 Avg =  1.5000 (actual),  2.7129 (predicted)
 Std =  0.5000 (actual),  0.4556 (predicted)
Pearson =  1.000000
Objective Function Score (Test) = nan

Fold: average
Model Parameters: 
Grid Objective Score (Train) = 
Objective Function Score (Test) = nan

