| algo_settings | If not defined SymbolicSolver.ALGO_SETTINGS is used
```python
algo_settings = {'neighbours_count':15, 'alpha':0.15, 'beta':0.5, 'pretest_size':1, 'sample_size':16}
```
- 'neighbours_count' : (int) Number tested neighbours in each iteration
- 'alpha' : (float) Score worsening limit for a iteration
- 'beta' : (float) Tree breadth-wise expanding factor in a range from 0 to 1
- 'pretest_size' : (int) Batch count(batch is 64 rows sample) for fast fitness preevaluating
- 'sample_size : (int) Number of batches of sample used to calculate the score during training | default: null |
| class_weight | Weights associated with classes in the form ``{class_label: weight}``
If not given, all classes are supposed to have weight one
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified | default: null |
| code_settings | If not defined SymbolicSolver.CODE_SETTINGS is used
```python
code_settings = {'min_size': 32, 'max_size':32, 'const_size':8}
```
- 'const_size' : (int) Maximum alloved constants in symbolic model, accept also 0
- 'min_size': (int) Minimum allowed equation size(as a linear program)
- 'max_size' : (int) Maximum allowed equation size(as a linear program) | default: null |
| const_settings | If not defined SymbolicSolver.CONST_SETTINGS is used
```python
const_settings = {'const_min':-LARGE_FLOAT, 'const_max':LARGE_FLOAT, 'predefined_const_prob':0.0, 'predefined_const_set': []}
```
- 'const_min' : (float) Lower range for constants used in equations
- 'const_max' : (float) Upper range for constants used in equations
- 'predefined_const_prob': (float) Probability of selecting one of the predefined constants during search process(mutation)
- 'predefined_const_set' : (array of floats) Predefined constants used during search process(mutation) | default: null |
| cv_params | If not defined SymbolicSolver.CLASSIFICATION_CV_PARAMS is used
```python
cv_params = {'n':0, 'cv_params':{}, 'select':'mean', 'opt_params':{'method': 'Nelder-Mead'}, 'opt_metric':make_scorer(log_loss, greater_is_better=False, needs_proba=True)}
```
- 'n' : (int) Crossvalidate n top models
- 'cv_params' : (dict) Parameters passed to scikit-learn cross_validate method
- select : (str) Best model selection method choose from 'mean'or 'median'
- opt_params : (dict) Parameters passed to scipy.optimize.minimize method
- opt_metric : (make_scorer) Scoring method | default: null |
| feature_probs | The probability that a mutation will select a feature | default: null |
| init_const_settings | If not defined SymbolicSolver.INIT_CONST_SETTINGS is used
```python
init_const_settings = {'const_min':-1.0, 'const_max':1.0, 'predefined_const_prob':0.0, 'predefined_const_set': []}
```
- 'const_min' : (float) Lower range for initializing constants
- 'const_max' : (float) Upper range for initializing constants
- 'predefined_const_prob': (float) Probability of selecting one of the predefined constants during initialization
- 'predefined_const_set' : (array of floats) Predefined constants used during initialization | default: null |
| iter_limit | Iterations limit. If is set to 0 there is no limit and the algorithm runs until time_limit is met | default: 0 |
| metric | Metric used for evaluating error. Choose from {'MSE', 'MAE', 'MSLE', 'LogLoss'} | default: "LogLoss" |
| num_threads | Number of used threads | default: 1 |
| population_settings | If not defined SymbolicSolver.POPULATION_SETTINGS is used
```python
population_settings = {'size': 64, 'tournament':4}
```
- 'size' : (int) Number of individuals in the population
- 'tournament' : (int) Tournament selection | default: null |
| precision | 'f64' or 'f32'. Internal floating number representation | default: "f32" |
| problem | Predefined instructions sets 'math' or 'simple' or 'fuzzy' or custom defines set of instructions with mutation probability
```python
problem={'add':10.0, 'mul':10.0, 'gt':1.0, 'lt':1.0, 'nop':1.0}
```
|**supported instructions**||
|-|-|
|**math**|add, sub, mul, div, pdiv, inv, minv, sq2, pow, exp, log, sqrt, cbrt, aq|
|**goniometric**|sin, cos, tan, asin, acos, atan, sinh, cosh, tanh|
|**other**|nop, max, min, abs, floor, ceil, lt, gt, lte, gte|
|**fuzzy**|f_and, f_or, f_xor, f_impl, f_not, f_nand, f_nor, f_nxor, f_nimpl|
*nop - no operation*
*pdiv - protected division*
*inv - inverse* $(-x)$
*minv - multiplicative inverse* $(1/x)$
*lt, gt, lte, gte -* $<, >, <=, >=$ | default: "math" |
| random_state | Random generator seed. If 0 then random generator will be initialized by system time | default: 0 |
| target_clip | Array of two float values clip_min and clip_max
If not defined SymbolicSolver.CLASSIFICATION_TARGET_CLIP is used
```python
target_clip=[3e-7, 1.0-3e-7]
``` | default: null |
| time_limit | Timeout in seconds. If is set to 0 there is no limit and the algorithm runs until iter_limit is met | default: 5.0 |
| transformation | Final transformation for computed value. Choose from { None, 'LOGISTIC', 'ORDINAL'} | default: "LOGISTIC" |
| verbose | Controls the verbosity when fitting and predicting | default: 0 |
| warm_start | | default: false |