# Baselines (recommendation)¶

## Global Average¶

Global Average uses the average rating value of all ratings to make predictions.

$\hat { r }_{ ui } = \mu$

### Example¶

  1 2 3 4 5 6 7 8 9 10 11 12  import Orange from orangecontrib.recommendation import GlobalAvgLearner # Load data and train the model data = Orange.data.Table('movielens100k.tab') learner = GlobalAvgLearner() recommender = learner(data) prediction = recommender(data[:3]) print(prediction) >>> [ 3.52986 3.52986 3.52986] 
class orangecontrib.recommendation.GlobalAvgLearner(preprocessors=None, verbose=False)[source]

Global Average

This model takes the average rating value of all ratings to make predictions.

Attributes:
verbose: boolean or int, optional
Prints information about the process according to the verbosity level. Values: False (verbose=0), True (verbose=1) and INTEGER
fit_storage(data)[source]

Fit the model according to the given training data.

Args:
data: Orange.data.Table
Returns:
self: object
Returns self.

## User Average¶

User Average uses the average rating value of a user to make predictions.

$\hat { r }_{ ui } = \mu_{u}$

### Example¶

  1 2 3 4 5 6 7 8 9 10 11 12 13 import Orange from orangecontrib.recommendation import UserAvgLearner # Load data and train the model data = Orange.data.Table('movielens100k.tab') learner = UserAvgLearner() recommender = learner(data) # Make predictions prediction = recommender(data[:3]) print(prediction) >>> [ 3.61538462 3.41304348 3.3515625 ] 
class orangecontrib.recommendation.UserAvgLearner(preprocessors=None, verbose=False)[source]

User average

This model takes the average rating value of a user to make predictions.

Attributes:
verbose: boolean or int, optional
Prints information about the process according to the verbosity level. Values: False (verbose=0), True (verbose=1) and INTEGER
fit_storage(data)[source]

Fit the model according to the given training data.

Args:
data: Orange.data.Table
Returns:
self: object
Returns self.

## Item Average¶

Item Average uses the average rating value of an item to make predictions.

$\hat { r }_{ ui } = \mu_{i}$

### Example¶

  1 2 3 4 5 6 7 8 9 10 11 12 13 import Orange from orangecontrib.recommendation import ItemAvgLearner # Load data and train the model data = Orange.data.Table('movielens100k.tab') learner = ItemAvgLearner() recommender = learner(data) # Make predictions prediction = recommender(data[:3]) print(prediction) >>> [ 3.99145299 4.16161616 2.15384615] 
class orangecontrib.recommendation.ItemAvgLearner(preprocessors=None, verbose=False)[source]

Item average

This model takes the average rating value of an item to make predictions.

Attributes:
verbose: boolean or int, optional
Prints information about the process according to the verbosity level. Values: False (verbose=0), True (verbose=1) and INTEGER
fit_storage(data)[source]

Fit the model according to the given training data.

Args:
data: Orange.data.Table
Returns:
self: object
Returns self.

## User-Item Baseline¶

User-Item Baseline takes the bias of users and items plus the global average to make predictions.

$\hat { r }_{ ui } = \mu + b_{u} + b_{i}$

### Example¶

  1 2 3 4 5 6 7 8 9 10 11 12 13  import Orange from orangecontrib.recommendation import UserItemBaselineLearner # Load data and train the model data = Orange.data.Table('movielens100k.tab') learner = UserItemBaselineLearner() recommender = learner(data) # Make predictions prediction = recommender(data[:3]) print(prediction) >>> [ 4.07697761 4.04479964 1.97554865] 
class orangecontrib.recommendation.UserItemBaselineLearner(preprocessors=None, verbose=False)[source]

User-Item baseline

This model takes the bias of users and items plus the global average to make predictions.

Attributes:
verbose: boolean or int, optional
Prints information about the process according to the verbosity level. Values: False (verbose=0), True (verbose=1) and INTEGER
fit_storage(data)[source]

Fit the model according to the given training data.

Args:
data: Orange.data.Table
Returns:
self: object
Returns self.