What is the best way to build a predictive lead scoring model using our existing free tools instead of buying specialized software?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the best way to build a predictive lead scoring model using our existing free tools instead of buying specialized software?
2 real searches · 1 real-search pass
free lead scoring model build with Google Sheets Python scikit-learn CRM historical conversion data train test split calibration precision recall guideGoogle ↗
OBSERVED
scikit-learn official user guide logistic regression preprocessing pipelines model evaluation precision recall calibrationGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
free lead scoring model build with Google Sheets Python scikit-learn CRM historical conversion data train test split calibration precision recall guide Google ↗
scikit-learn official user guide logistic regression preprocessing pipelines model evaluation precision recall calibration Google ↗
- Open Google Colab · colab.research.google.com
- Python website · python.org
- Documentation · scikit-learn.org
- Documentation · pandas.pydata.org
- scikit-learn/doc/getting_started.rst at main · scikit-learn/scikit-learn · github.com
- LogisticRegression · scikit-learn.org
- scikit-learn/doc/modules/model_evaluation.rst at main · scikit-learn/scikit-learn · github.com
- scikit-learn/doc/modules/calibration.rst at main · scikit-learn/scikit-learn · github.com
- Google Sheets · sheets.google.com
- Looker Studio · lookerstudio.google.com
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
free lead scoring model build with Google Sheets Python scikit-learn CRM historical conversion data train test split calibration precision recall guide OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
scikit-learn official user guide logistic regression preprocessing pipelines model evaluation precision recall calibration OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |