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Predictive Query
Python SDK
Model Plan

Overview
ASSUMING
CLASSIFY/RANK TOP K
FOR EACH
PREDICT
WHERE
AVG
COUNT
COUNT_DISTINCT
FIRST
LAST
LIST_DISTINCT
MAX
MIN
SUM
!=
<
<=
=
>
>=
AND
CONTAINS
ENDS WITH
IN
IS NOT NULL
IS NULL
LIKE
NOT LIKE
NOT CONTAINS
NOT
OR
STARTS WITH
kumoai
kumoai.connector
kumoai.graph
kumoai.pquery
kumoai.encoder
kumoai.trainer
kumoai.utils
kumoai.rfm
Column Processing
GNN architecture
aggregation
channels
Graph Transformer architecture
channels
num_layers
num_heads
dropout
positional_encodings
Selecting Link Prediction Model Architectures on Kumo
activation
distance_measure
handle_new_target_entities
module
normalization
num_post_message_passing_layers
num_pre_message_passing_layers
ranking_embedding_loss_coeff
output_embedding_dim
prediction_time_encodings
target_embedding_mode
use_seq_id
num_neighbors
sample_from_entity_table
base_lr
batch_size
early_stopping
lr_scheduler
loss
majority_sampling_ratio
max_epochs
max_steps_per_epoch
max_test_steps
max_val_steps
weight_decay
weight_mode
refit_full
refit_trainval
metrics
num_experiments
tune_metric
end_time
forecast_length
forecast_type
lag_timesteps
year_over_year
weight_col
split
start_time
timeframe_step
train_end_offset
train_start_offset

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Model Plan Training Job Plan

refit_full

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