I’m trying to use `SymbolicRegression`

from Julia, based on some [`SRRegressor`

] model in Python. Here is the Python model:

```
model_SK = PySRRegressor(
batching=True,
niterations=400,
model_selection="accuracy",
binary_operators=["+", "-", "*", "/", "^"],
unary_operators=["log", "tan"],
nested_constraints={'tan': {'tan': 0}, 'log': {'log': 0}},
maxsize=30,
timeout_in_seconds=60 * 5,
)
```

The following Julia code works – but I’ve had to skip 2 keywords…:

```
model_SK = SRRegressor(
batching=true,
niterations=400,
#model_selection=:accuracy,
binary_operators=[+,-,*,/,^],
unary_operators=[log, tan],
#nested_constraints={tan: {tan: 0}, log: {log: 0}},
maxsize=30,
timeout_in_seconds=60.0 * 5,
)
```

I don’t find (Julia) documentation for the (Python) keywords `model_selection`

and `nested_constraints`

…, nor what the right-hand side means :-o.

*Questions*:

A. What are the keyword names in Julia for `model_selection`

and `nested_constraints`

?

B. What would be the Julia equivalence of their RHS values (i.e., `"accuracy"`

and `{tan: {tan: 0}, log: {log: 0}}`

, respectively)?

Without these two keywords, the data vs. prediction is still pretty good: