reformat outlook
}
],
"source": [
- "optimizer = Optimizer(algorithm=AdamSolver(learning_rate=0.005, learning_rate_policy='step', gamma=0.9, step_size=5),\n",
+ "optimizer = Optimizer(algorithm=AdamSolver(learning_rate=0.005, learning_rate_policy='step', \n",
+ " gamma=0.9, step_size=5), \n",
" mini_batch_size=4, seed=1234, max_epochs=50, log_level=2)\n",
"\n",
"result = model1.fit(train_tbl, inputs=bc_x_df.columns.tolist(), nominals=['target_class'], \n",
],
"source": [
"# Plot the precision recall curve for target_class = 1. \n",
- "plot_precision_recall(test_result_table, 'target_class', 'P_target_class1', pos_label=1, figsize=(6,6), linewidth=2)"
+ "plot_precision_recall(test_result_table, 'target_class', 'P_target_class1', pos_label=1, figsize=(6,6), \n",
+ " linewidth=2)"
]
},
{
