Why is performance worse when my time-series data is not shuffled prior to a train/test split vs. when it is...
$begingroup$
We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
edited 16 hours ago
Wes
31511
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
|
show 2 more comments
$begingroup$
We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
edited 16 hours ago
Wes
31511
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
Can you show a plot of the entire data set in both cases (including the training set, not just the test set)?
$endgroup$
– Wes
19 hours ago
$begingroup$
Also, can you give the actual performance of the models, and not just the predictions (i.e., a numerical value like MSE, etc.)?
$endgroup$
– Wes
19 hours ago
$begingroup$
When shuffle = True, 'mae': 0.012749809403589319, 'r2score':0.534131151271332705, 'rmse': 0.01478679726017944. When shuffle = False, 'mae': 0.012631170478535453, 'r2score': -0.03146366881412077, 'rmse': 0.020236256497426223 Links for training set plots, shuffle = False : i.imgur.com/GYAQup9.png, shuffle = True : i.imgur.com/b9cATse.png
$endgroup$
– Sumesh Surendran
12 hours ago
$begingroup$
What is happening when the target variable is 0? Is this a valid result? You have a short section of 0 all in a row before it is shuffled.
$endgroup$
– Wes
5 hours ago
$begingroup$
Also, it is probably useful for you to look at histograms of your features and target variables in the training set vs. the test set in both cases of not shuffling and shuffling.
$endgroup$
– Wes
5 hours ago
|
show 2 more comments
$begingroup$
We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
edited 16 hours ago
Wes
31511
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
We are running RandomForest model on a time-series data. The model is run in real time and is refit every time a new row is added. Since it is a timeseries data, we set shuffle to false while splitting into train and test dataset.
We observed that there is a drastic change in scores when shuffle is True and when shuffle is false
The code being used is as follows
# Set shuffle = 'True' or 'False'
df = pandas.read_csv('data.csv', index_col=0)
X = df.drop(columns=['label'])
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, shuffle=True)
count = 0
predictions =
for idx in X_test.index.values:
# Train the model on training data
# print(count, X_train.shape, y_train.shape)
rf = RandomForestRegressor(n_estimators = 600, max_depth = 7, random_state = 12345)
rf.fit(X_train, y_train)
predictions.append(rf.predict(X_test.loc[X_test.index == idx]))
# print(len(predictions))
X_train.loc[len(X_train)] = X_test.loc[idx]
y_train.loc[len(y_train)] = y_test.loc[idx]
count+=1
Initially, we thought the difference is due to covariance shift in the data. But that shouldn't affect this much for continuous fit
Here are screenshots for the prediction plot
With shuffle = False
With shuffle = True
time-series predictive-modeling random-forest training transfer-learning
time-series predictive-modeling random-forest training transfer-learning
edited 16 hours ago
Wes
31511
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 16 hours ago
Wes
31511
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 16 hours ago
Wes
31511
edited 16 hours ago
Wes
31511
edited 16 hours ago
Wes
31511
31511
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
asked 2 days ago
Sumesh SurendranSumesh Surendran
113
113
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sumesh Surendran is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Can you show a plot of the entire data set in both cases (including the training set, not just the test set)?
$endgroup$
– Wes
19 hours ago
$begingroup$
Also, can you give the actual performance of the models, and not just the predictions (i.e., a numerical value like MSE, etc.)?
$endgroup$
– Wes
19 hours ago
$begingroup$
When shuffle = True, 'mae': 0.012749809403589319, 'r2score':0.534131151271332705, 'rmse': 0.01478679726017944. When shuffle = False, 'mae': 0.012631170478535453, 'r2score': -0.03146366881412077, 'rmse': 0.020236256497426223 Links for training set plots, shuffle = False : i.imgur.com/GYAQup9.png, shuffle = True : i.imgur.com/b9cATse.png
$endgroup$
– Sumesh Surendran
12 hours ago
$begingroup$
What is happening when the target variable is 0? Is this a valid result? You have a short section of 0 all in a row before it is shuffled.
$endgroup$
– Wes
5 hours ago
$begingroup$
Also, it is probably useful for you to look at histograms of your features and target variables in the training set vs. the test set in both cases of not shuffling and shuffling.
$endgroup$
– Wes
5 hours ago
|
show 2 more comments
$begingroup$
Can you show a plot of the entire data set in both cases (including the training set, not just the test set)?
$endgroup$
– Wes
19 hours ago
$begingroup$
Also, can you give the actual performance of the models, and not just the predictions (i.e., a numerical value like MSE, etc.)?
$endgroup$
– Wes
19 hours ago
$begingroup$
When shuffle = True, 'mae': 0.012749809403589319, 'r2score':0.534131151271332705, 'rmse': 0.01478679726017944. When shuffle = False, 'mae': 0.012631170478535453, 'r2score': -0.03146366881412077, 'rmse': 0.020236256497426223 Links for training set plots, shuffle = False : i.imgur.com/GYAQup9.png, shuffle = True : i.imgur.com/b9cATse.png
$endgroup$
– Sumesh Surendran
12 hours ago
$begingroup$
What is happening when the target variable is 0? Is this a valid result? You have a short section of 0 all in a row before it is shuffled.
$endgroup$
– Wes
5 hours ago
$begingroup$
Also, it is probably useful for you to look at histograms of your features and target variables in the training set vs. the test set in both cases of not shuffling and shuffling.
$endgroup$
– Wes
5 hours ago
$begingroup$
Can you show a plot of the entire data set in both cases (including the training set, not just the test set)?
$endgroup$
– Wes
19 hours ago
$begingroup$
Can you show a plot of the entire data set in both cases (including the training set, not just the test set)?
$endgroup$
– Wes
19 hours ago
$begingroup$
Also, can you give the actual performance of the models, and not just the predictions (i.e., a numerical value like MSE, etc.)?
$endgroup$
– Wes
19 hours ago
$begingroup$
Also, can you give the actual performance of the models, and not just the predictions (i.e., a numerical value like MSE, etc.)?
$endgroup$
– Wes
19 hours ago
$begingroup$
When shuffle = True, 'mae': 0.012749809403589319, 'r2score':0.534131151271332705, 'rmse': 0.01478679726017944. When shuffle = False, 'mae': 0.012631170478535453, 'r2score': -0.03146366881412077, 'rmse': 0.020236256497426223 Links for training set plots, shuffle = False : i.imgur.com/GYAQup9.png, shuffle = True : i.imgur.com/b9cATse.png
$endgroup$
– Sumesh Surendran
12 hours ago
$begingroup$
When shuffle = True, 'mae': 0.012749809403589319, 'r2score':0.534131151271332705, 'rmse': 0.01478679726017944. When shuffle = False, 'mae': 0.012631170478535453, 'r2score': -0.03146366881412077, 'rmse': 0.020236256497426223 Links for training set plots, shuffle = False : i.imgur.com/GYAQup9.png, shuffle = True : i.imgur.com/b9cATse.png
$endgroup$
– Sumesh Surendran
12 hours ago
$begingroup$
What is happening when the target variable is 0? Is this a valid result? You have a short section of 0 all in a row before it is shuffled.
$endgroup$
– Wes
5 hours ago
$begingroup$
What is happening when the target variable is 0? Is this a valid result? You have a short section of 0 all in a row before it is shuffled.
$endgroup$
– Wes
5 hours ago
$begingroup$
Also, it is probably useful for you to look at histograms of your features and target variables in the training set vs. the test set in both cases of not shuffling and shuffling.
$endgroup$
– Wes
5 hours ago
$begingroup$
Also, it is probably useful for you to look at histograms of your features and target variables in the training set vs. the test set in both cases of not shuffling and shuffling.
$endgroup$
– Wes
5 hours ago
|
show 2 more comments
1 Answer
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Without looking further into the data myself, I can surmise that something has changed recently with your data such that if you split without shuffling, some aspect of the data in your test set (which is what you most recently collected) is underrepresented in your training set. By shuffling the data, you allow those more recent samples to also be present in your training set, and thus your test set performance improves.
answered 57 mins ago
WesWes
31511
$endgroup$
add a comment |
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$begingroup$
Without looking further into the data myself, I can surmise that something has changed recently with your data such that if you split without shuffling, some aspect of the data in your test set (which is what you most recently collected) is underrepresented in your training set. By shuffling the data, you allow those more recent samples to also be present in your training set, and thus your test set performance improves.
answered 57 mins ago
WesWes
31511
$endgroup$
add a comment |
$begingroup$
Without looking further into the data myself, I can surmise that something has changed recently with your data such that if you split without shuffling, some aspect of the data in your test set (which is what you most recently collected) is underrepresented in your training set. By shuffling the data, you allow those more recent samples to also be present in your training set, and thus your test set performance improves.
answered 57 mins ago
WesWes
31511
$endgroup$
add a comment |
$begingroup$
Without looking further into the data myself, I can surmise that something has changed recently with your data such that if you split without shuffling, some aspect of the data in your test set (which is what you most recently collected) is underrepresented in your training set. By shuffling the data, you allow those more recent samples to also be present in your training set, and thus your test set performance improves.
answered 57 mins ago
WesWes
31511
$endgroup$
Without looking further into the data myself, I can surmise that something has changed recently with your data such that if you split without shuffling, some aspect of the data in your test set (which is what you most recently collected) is underrepresented in your training set. By shuffling the data, you allow those more recent samples to also be present in your training set, and thus your test set performance improves.
answered 57 mins ago
WesWes
31511
answered 57 mins ago
WesWes
31511
answered 57 mins ago
WesWes
31511
answered 57 mins ago
WesWes
31511
31511
add a comment |
add a comment |
Sumesh Surendran is a new contributor. Be nice, and check out our Code of Conduct.
Sumesh Surendran is a new contributor. Be nice, and check out our Code of Conduct.
Sumesh Surendran is a new contributor. Be nice, and check out our Code of Conduct.
Sumesh Surendran is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
Can you show a plot of the entire data set in both cases (including the training set, not just the test set)?
$endgroup$
– Wes
19 hours ago
$begingroup$
Also, can you give the actual performance of the models, and not just the predictions (i.e., a numerical value like MSE, etc.)?
$endgroup$
– Wes
19 hours ago
$begingroup$
When shuffle = True, 'mae': 0.012749809403589319, 'r2score':0.534131151271332705, 'rmse': 0.01478679726017944. When shuffle = False, 'mae': 0.012631170478535453, 'r2score': -0.03146366881412077, 'rmse': 0.020236256497426223 Links for training set plots, shuffle = False : i.imgur.com/GYAQup9.png, shuffle = True : i.imgur.com/b9cATse.png
$endgroup$
– Sumesh Surendran
12 hours ago
$begingroup$
What is happening when the target variable is 0? Is this a valid result? You have a short section of 0 all in a row before it is shuffled.
$endgroup$
– Wes
5 hours ago
$begingroup$
Also, it is probably useful for you to look at histograms of your features and target variables in the training set vs. the test set in both cases of not shuffling and shuffling.
$endgroup$
– Wes
5 hours ago