Surprise
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User Guide
Getting Started
Using prediction algorithms
How to build you own prediction algorithm
Notation standards, References
API Reference
prediction_algorithms package
similarities module
accuracy module
dataset module
evaluate module
dump module
Surprise
Docs
»
Index
A
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B
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C
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D
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E
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F
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G
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I
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K
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L
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M
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N
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P
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R
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S
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T
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U
A
AlgoBase (class in surprise.prediction_algorithms.algo_base)
all_items() (surprise.dataset.Trainset method)
all_ratings() (surprise.dataset.Trainset method)
all_users() (surprise.dataset.Trainset method)
B
BaselineOnly (class in surprise.prediction_algorithms.baseline_only)
best_estimator (surprise.evaluate.GridSearch attribute)
best_index (surprise.evaluate.GridSearch attribute)
best_params (surprise.evaluate.GridSearch attribute)
best_score (surprise.evaluate.GridSearch attribute)
build_full_trainset() (surprise.dataset.DatasetAutoFolds method)
C
CoClustering (class in surprise.prediction_algorithms.co_clustering)
compute_baselines() (surprise.prediction_algorithms.algo_base.AlgoBase method)
compute_similarities() (surprise.prediction_algorithms.algo_base.AlgoBase method)
cosine() (in module surprise.similarities)
cv_results (surprise.evaluate.GridSearch attribute)
D
Dataset (class in surprise.dataset)
DatasetAutoFolds (class in surprise.dataset)
dump() (in module surprise.dump)
E
evaluate() (in module surprise.evaluate)
(surprise.evaluate.GridSearch method)
F
fcp() (in module surprise.accuracy)
folds() (surprise.dataset.Dataset method)
G
global_mean (surprise.dataset.Trainset attribute)
,
[1]
GridSearch (class in surprise.evaluate)
I
ir (surprise.dataset.Trainset attribute)
K
KNNBaseline (class in surprise.prediction_algorithms.knns)
KNNBasic (class in surprise.prediction_algorithms.knns)
KNNWithMeans (class in surprise.prediction_algorithms.knns)
knows_item() (surprise.dataset.Trainset method)
knows_user() (surprise.dataset.Trainset method)
L
load_builtin() (surprise.dataset.Dataset class method)
load_from_file() (surprise.dataset.Dataset class method)
load_from_folds() (surprise.dataset.Dataset class method)
M
mae() (in module surprise.accuracy)
msd() (in module surprise.similarities)
N
n_items (surprise.dataset.Trainset attribute)
n_ratings (surprise.dataset.Trainset attribute)
n_users (surprise.dataset.Trainset attribute)
NMF (class in surprise.prediction_algorithms.matrix_factorization)
NormalPredictor (class in surprise.prediction_algorithms.random_pred)
P
pearson() (in module surprise.similarities)
pearson_baseline() (in module surprise.similarities)
predict() (surprise.prediction_algorithms.algo_base.AlgoBase method)
Prediction (class in surprise.prediction_algorithms.predictions)
PredictionImpossible
R
rating_scale (surprise.dataset.Trainset attribute)
Reader (class in surprise.dataset)
rmse() (in module surprise.accuracy)
S
SlopeOne (class in surprise.prediction_algorithms.slope_one)
split() (surprise.dataset.DatasetAutoFolds method)
surprise.accuracy (module)
surprise.dataset (module)
surprise.dump (module)
surprise.evaluate (module)
surprise.prediction_algorithms (module)
surprise.prediction_algorithms.algo_base (module)
surprise.prediction_algorithms.predictions (module)
surprise.similarities (module)
SVD (class in surprise.prediction_algorithms.matrix_factorization)
SVDpp (class in surprise.prediction_algorithms.matrix_factorization)
T
test() (surprise.prediction_algorithms.algo_base.AlgoBase method)
to_inner_iid() (surprise.dataset.Trainset method)
to_inner_uid() (surprise.dataset.Trainset method)
train() (surprise.prediction_algorithms.algo_base.AlgoBase method)
Trainset (class in surprise.dataset)
U
ur (surprise.dataset.Trainset attribute)