How SHAP value explains contribution of features for outliers event?

I'm trying to understand and experiment with how the SHAP value can explain behaviour for each outlier events (rows) and how it can be related to shap.force_plot(). I already created a simple synthetic dataset with 7 outliers.

• I didn't get how 4.85 calculated as output model.

outliers_df = temp_dataset.loc[synth_data_df[outdet.label_feature] == 1]
outliers_df

#Here is the frame of 7 outlier cases I input to SHAP: 

+----+-------------+--------------+---------------+
| id |      NF1    |      CF1     |outlier_scores |
+----+-------------+--------------+---------------+
|  1 |       904   |     2        | -0.192221     |
|  2 |       951   |     3        | -0.203116     |
|  3 |       125   |     5        | -0.076722     |
|  4 |       755   |     2        | -0.237189     |
|  5 |       983   |     2        | -0.213205     |
|  6 |       906   |     3        | -0.202074     |
|  7 |       999   |     5        | -0.266428     |
+----+-------------+--------------+---------------+

I applied IsolationForest using class OutlierDetector in my implementation to isolate outliers:

outdet = OutlierDetector(synth_data_df, label_feature=label, 
                         normalize_outlier_scores=True, standardize_outlier_scores=False, 
                         n_estimators=100, max_samples=1., contamination=0.1)
outdet.train_IF()

I got SHAP for each rows:

outdet.shap_values_outliers = outdet.explainer.shap_values(outliers_df)
outdet.shap_values_outliers
array([[-6.0205, -0.7785,  0.    ],
       [-6.3379, -0.7031,  0.    ],
       [-2.9014, -1.0911,  0.    ],
       [-6.6259, -1.1502,  0.    ],
       [-6.445 , -0.8171,  0.    ],
       [-6.2393, -0.7787,  0.    ],
       [-8.1958, -0.1862,  0.    ]])

This is simple shap.force_plot() for first row for 3 features. I already concatenate outlier_scores to the frame after I trained my model with NF1 and CF1 as follow:

• How to get $\text{basic}_\text{value} = 11.65$ via a script, as explained here?

• General interpretation considering $\text{basic}_\text{value} = 11.65$ and $\text{Model}_\text{output} = 4.85$ for first event or observation considering no features pushing the prediction higher are shownred by red color?

• Reason for getting $\text{SHAP}_\text{outlier_scores} = 0$ ?

Edit I would also like to share the following plot, which may help to see if we can understand the contribution of one of these two features to explain outliers like $\text{SHAP}_\text{NF1} = [-8, -6]$:

outdet.plot_shap_values()