import pandas as pd                #data loading and manipulation
import matplotlib.pyplot as plt     #ploting
import seaborn as sns              #statistical plotiing

diabetes = pd.read_csv("diabetes.csv")
diabetes.head()
diabetes.info()

from pycaret.classification import *

dia_clf = setup(data = diabetes,
                 target =  'diabetes',
                 numeric_features=["pregnant"],
                 train_size = 0.8,
                 normalize=True,
                 session_id=123)

tune_gb = tune_model("xgboost", optimize = "AUC", n_iter = 500)
tune_xgb
plot_model(tuned_xgb, plot='feature')
plot_model(tuned_xgb, plot = 'auc')
plot_model(tuned_xgb, plot = 'pr')

predict_model(tuned_xgb)
final_gbc = finalize_model(tuned_xgb)

save_model(tuned_xgb,'Final tuned_xgb Model 11July2020')
saved_final_lightxgb = load_model('Final tuned_xgb Model 11July2020')

Output:
Description	Value
0	session_id	123
1	Target	diabetes
2	Target Type	Binary
3	Label Encoded	neg: 0, pos: 1
4	Original Data	(392, 9)
5	Missing Values	False
6	Numeric Features	8
7	Categorical Features	0
8	Ordinal Features	False
9	High Cardinality Features	False
10	High Cardinality Method	None
11	Transformed Train Set	(313, 8)
12	Transformed Test Set	(79, 8)
13	Shuffle Train-Test	True
14	Stratify Train-Test	False
15	Fold Generator	StratifiedKFold
16	Fold Number	10
17	CPU Jobs	-1
18	Use GPU	False
19	Log Experiment	False
20	Experiment Name	clf-default-name
21	USI	ca0c
22	Imputation Type	simple
23	Iterative Imputation Iteration	None
24	Numeric Imputer	mean
25	Iterative Imputation Numeric Model	None
26	Categorical Imputer	constant
27	Iterative Imputation Categorical Model	None
28	Unknown Categoricals Handling	least_frequent
29	Normalize	True
30	Normalize Method	zscore
31	Transformation	False
32	Transformation Method	None
33	PCA	False
34	PCA Method	None
35	PCA Components	None
36	Ignore Low Variance	False
37	Combine Rare Levels	False
38	Rare Level Threshold	None
39	Numeric Binning	False
40	Remove Outliers	False
41	Outliers Threshold	None
42	Remove Multicollinearity	False
43	Multicollinearity Threshold	None
44	Clustering	False
45	Clustering Iteration	None
46	Polynomial Features	False
47	Polynomial Degree	None
48	Trignometry Features	False
49	Polynomial Threshold	None
50	Group Features	False
51	Feature Selection	False
52	Feature Selection Method	classic
53	Features Selection Threshold	None
54	Feature Interaction	False
55	Feature Ratio	False
56	Interaction Threshold	None
57	Fix Imbalance	False
58	Fix Imbalance Method	SMOTE

Error:
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-27b2d1513045> in <module>
     16                  session_id=123)
     17 
---> 18 tune_gb = tune_model("xgboost", optimize = "AUC", n_iter = 500)
     19 tune_xgb
     20 plot_model(tuned_xgb, plot='feature')

~\miniconda3\envs\pycaret_env\lib\site-packages\pycaret\classification.py in tune_model(estimator, fold, round, n_iter, custom_grid, optimize, custom_scorer, search_library, search_algorithm, early_stopping, early_stopping_max_iters, choose_better, fit_kwargs, groups, return_tuner, verbose, tuner_verbose, **kwargs)
   1100         verbose=verbose,
   1101         tuner_verbose=tuner_verbose,
-> 1102         **kwargs,
   1103     )
   1104 

~\miniconda3\envs\pycaret_env\lib\site-packages\pycaret\internal\tabular.py in tune_model_supervised(estimator, fold, round, n_iter, custom_grid, optimize, custom_scorer, search_library, search_algorithm, early_stopping, early_stopping_max_iters, choose_better, fit_kwargs, groups, return_tuner, verbose, tuner_verbose, display, **kwargs)
   3787     if type(estimator) is str:
   3788         raise TypeError(
-> 3789             "The behavior of tune_model in version 1.0.1 is changed. Please pass trained model object."
   3790         )
   3791 

TypeError: The behavior of tune_model in version 1.0.1 is changed. Please pass trained model object.