<jupyter_start><jupyter_text>Default MLFlow Logging<jupyter_code>#### Setup experiment with MLFlow logging ----
exp.setup(
    data=y,
    fh=12,
    session_id=42,
    log_experiment=True,
    experiment_name="my_exp",
  )
## Run from terminal that has the python/anaconda environment.
## Alternately, run from notebook as below
## Web browser must be accessible to see results.
# !mlflow ui
#### Create a model ----
model = exp.create_model("lr_cds_dt")
#### Make predictions on holdout set ----
exp.predict_model(model)<jupyter_output><empty_output><jupyter_text>MLFlow Logging with Plots<jupyter_code>#### Setup experiment with MLFlow logging ----
#### This time, also enable logging of (default) plots ----
exp.setup(
    data=y,
    fh=12,
    session_id=42,
    log_experiment=True,
    experiment_name="my_exp_with_plots",
    log_plots=True
  )
model = exp.create_model("lr_cds_dt")<jupyter_output><empty_output><jupyter_text>ML Flow Logging with Custom Plots<jupyter_code>#### Setup experiment with MLFlow logging ----
#### This time, also enable logging of plots (custom) ----
exp.setup(
    data=y,
    fh=12,
    session_id=42,
    log_experiment=True,
    experiment_name="my_exp_with_custom",
    log_plots=["ts", "train_test_split", "cv", "forecast"]
  )
model = exp.create_model("lr_cds_dt")<jupyter_output><empty_output>
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{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '110271', 'license': 'MIT', 'n_tokens_mistral': 476, 'n_tokens_neox': 456, 'n_words': 104, 'path': 'time_series/pycaret_mlflow_basics.ipynb', 'repo_name': 'ngupta23/pycaret_faqs', 'size': '1338'}