Python: Imbalanced data for XGBoost Multi-label classification
I have a dataset of a stock's returns where the Y-label is price change direction (= 2 if upward tick, = 1 if downward tick, and = 0 if no move. Some of the features, X, include the lagged label values (i.e. the previous day's price direction change).
I am trying to run the XGBoost classification model, however my data is highly imbalanced. Most of the Y label values are = 0 meaning the stock price did not move.
How can I incorporate this imbalance in a multi-label XGBoost classification problem?
My code is the following:
X = df[["ret_D_lag_1", "ret_D_lag_2", "ret_D_lag_3"]]
y = df["ret_D_t1"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# use DMatrix for xgboost
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
# set xgboost params
param = {
'max_depth': 3, # the maximum depth of each tree
'eta': 0.3, # the training step for each iteration
'silent': 1, # logging mode - quiet
'objective': 'multi:softprob', # error evaluation for multiclass training
'num_class': 3} # the number of classes that exist in this datset
num_round = 20 # the number of training iterations
# Train the model
bst = xgb.train(param, dtrain, num_round)
# Predict and choose highest probability for each label
preds = bst.predict(dtest)
best_preds = np.asarray([np.argmax(line) for line in preds])
machine-learning classification xgboost multilabel-classification
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
asked Nov 16 '18 at 0:25
rolandroland
3384618
add a comment |
I have a dataset of a stock's returns where the Y-label is price change direction (= 2 if upward tick, = 1 if downward tick, and = 0 if no move. Some of the features, X, include the lagged label values (i.e. the previous day's price direction change).
I am trying to run the XGBoost classification model, however my data is highly imbalanced. Most of the Y label values are = 0 meaning the stock price did not move.
How can I incorporate this imbalance in a multi-label XGBoost classification problem?
My code is the following:
X = df[["ret_D_lag_1", "ret_D_lag_2", "ret_D_lag_3"]]
y = df["ret_D_t1"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# use DMatrix for xgboost
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
# set xgboost params
param = {
'max_depth': 3, # the maximum depth of each tree
'eta': 0.3, # the training step for each iteration
'silent': 1, # logging mode - quiet
'objective': 'multi:softprob', # error evaluation for multiclass training
'num_class': 3} # the number of classes that exist in this datset
num_round = 20 # the number of training iterations
# Train the model
bst = xgb.train(param, dtrain, num_round)
# Predict and choose highest probability for each label
preds = bst.predict(dtest)
best_preds = np.asarray([np.argmax(line) for line in preds])
machine-learning classification xgboost multilabel-classification
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
asked Nov 16 '18 at 0:25
rolandroland
3384618
add a comment |
I have a dataset of a stock's returns where the Y-label is price change direction (= 2 if upward tick, = 1 if downward tick, and = 0 if no move. Some of the features, X, include the lagged label values (i.e. the previous day's price direction change).
I am trying to run the XGBoost classification model, however my data is highly imbalanced. Most of the Y label values are = 0 meaning the stock price did not move.
How can I incorporate this imbalance in a multi-label XGBoost classification problem?
My code is the following:
X = df[["ret_D_lag_1", "ret_D_lag_2", "ret_D_lag_3"]]
y = df["ret_D_t1"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# use DMatrix for xgboost
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
# set xgboost params
param = {
'max_depth': 3, # the maximum depth of each tree
'eta': 0.3, # the training step for each iteration
'silent': 1, # logging mode - quiet
'objective': 'multi:softprob', # error evaluation for multiclass training
'num_class': 3} # the number of classes that exist in this datset
num_round = 20 # the number of training iterations
# Train the model
bst = xgb.train(param, dtrain, num_round)
# Predict and choose highest probability for each label
preds = bst.predict(dtest)
best_preds = np.asarray([np.argmax(line) for line in preds])
machine-learning classification xgboost multilabel-classification
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
asked Nov 16 '18 at 0:25
rolandroland
3384618
I have a dataset of a stock's returns where the Y-label is price change direction (= 2 if upward tick, = 1 if downward tick, and = 0 if no move. Some of the features, X, include the lagged label values (i.e. the previous day's price direction change).
I am trying to run the XGBoost classification model, however my data is highly imbalanced. Most of the Y label values are = 0 meaning the stock price did not move.
How can I incorporate this imbalance in a multi-label XGBoost classification problem?
My code is the following:
X = df[["ret_D_lag_1", "ret_D_lag_2", "ret_D_lag_3"]]
y = df["ret_D_t1"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# use DMatrix for xgboost
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
# set xgboost params
param = {
'max_depth': 3, # the maximum depth of each tree
'eta': 0.3, # the training step for each iteration
'silent': 1, # logging mode - quiet
'objective': 'multi:softprob', # error evaluation for multiclass training
'num_class': 3} # the number of classes that exist in this datset
num_round = 20 # the number of training iterations
# Train the model
bst = xgb.train(param, dtrain, num_round)
# Predict and choose highest probability for each label
preds = bst.predict(dtest)
best_preds = np.asarray([np.argmax(line) for line in preds])
machine-learning classification xgboost multilabel-classification
machine-learning classification xgboost multilabel-classification
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
asked Nov 16 '18 at 0:25
rolandroland
3384618
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
asked Nov 16 '18 at 0:25
rolandroland
3384618
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
edited Nov 16 '18 at 11:15
Sreeram TP
3,09631440
3,09631440
asked Nov 16 '18 at 0:25
rolandroland
3384618
asked Nov 16 '18 at 0:25
rolandroland
3384618
asked Nov 16 '18 at 0:25
rolandroland
3384618
3384618
add a comment |
add a comment |
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