Comments (7)
JoaquinAmatRodrigo
commented on May 24, 2024
Hola @GabrielCornejo,
TimeSeriesDifferentiator está disponible a partir de la version de skforecast 0.10.0
from skforecast.
GabrielCornejo
commented on May 24, 2024
Muchas gracias, lo acabo de arreglar.
from skforecast.
GabrielCornejo
commented on May 24, 2024
Tengo otra pregunta, ¿cómo puedo acceder a ver algunos de árboles (gráficos) de un random forest, por ejemplo, si pedí 1000 árboles, quiero ver el 35, o el 567, etc?
from skforecast.
JavierEscobarOrtiz
commented on May 24, 2024
Hola @GabrielCornejo,
Esto se sale del ámbito de skforecast y nos gustaría mantener este espacio para issues o preguntas relacionadas con el proyecto.
No obstante, nuestro amigo ChatGPT nos ha ayudado con tu pregunta:
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import plot_tree
# Load data
iris = load_iris()
X, y = iris.data, iris.target
# Train random forest classifier
rf = RandomForestClassifier(n_estimators=10, random_state=42)
rf.fit(X, y)
# Plot a tree from the forest
tree_idx = 0
plt.figure(figsize=(20,10))
plot_tree(rf.estimators_[tree_idx], filled=True,
feature_names=iris.feature_names, class_names=list(iris.target_names))
plt.show()
Cambiando el tree_idx puedes acceder al árbol que quieras.
Espero que te ayude!
from skforecast.
GabrielCornejo
commented on May 24, 2024
Hola:
He estado tratando de "ver" alguno de los árboles generados por el Random
Forest Regressor en skforecast luego de usar hiperparámetros, pero no he
podido. Les muestro mis resultados a ver si me pueden ayudar.
Muchas gracias.
[image: image.png]
El código es muy parecido al que me enviaron, también lo obtuve de CHATGPT.
En el mensaje de error menciona que ForecasteAutorreg no tiene el atributo
'estimators_'
¿Puede que se guarde con otro nombre?
Saludos.
El vie, 15 sept 2023 a las 9:19, Javier Escobar Ortiz (<
***@***.***>) escribió:
… Hola @GabrielCornejo <https://github.com/GabrielCornejo>,
Esto se sale del ámbito de skforecast y nos gustaría mantener este espacio
para issues o preguntas relacionadas con el proyecto.
No obstante, nuestro amigo ChatGPT nos ha ayudado con tu pregunta:
import matplotlib.pyplot as pltfrom sklearn.datasets import load_irisfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.tree import plot_tree
# Load datairis = load_iris()X, y = iris.data, iris.target
# Train random forest classifierrf = RandomForestClassifier(n_estimators=10, random_state=42)rf.fit(X, y)
# Plot a tree from the foresttree_idx = 0plt.figure(figsize=(20,10))plot_tree(rf.estimators_[tree_idx], filled=True,
feature_names=iris.feature_names, class_names=list(iris.target_names))plt.show()
Cambiando el tree_idx puedes acceder al árbol que quieras.
Espero que te ayude!
—
Reply to this email directly, view it on GitHub
<#543 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AC63YQ3JAZM6QADC7FRKYRDX2RBT5ANCNFSM6AAAAAA4YYYSUI>
.
You are receiving this because you were mentioned.Message ID:
***@***.***>
from skforecast.
JoaquinAmatRodrigo
commented on May 24, 2024
Hola @GabrielCornejo,
Para acceder a los atributos del regressor de un forecaster tienes que utilizar forecaster.regressor.<atributo>, por ejemplo forecaster.regressor.estimators_.
from skforecast.
GabrielCornejo
commented on May 24, 2024
Hola Joaquín.
Gracias por la aclaración. Ahora funciona.
Saludos.
Gabriel.
El dom, 24 de sept de 2023, 17:10, Joaquín Amat Rodrigo <
***@***.***> escribió:
… Hola @GabrielCornejo <https://github.com/GabrielCornejo>,
Para acceder a los atributos del regressor de un forecaster tienes que
utilizar forecaster.regressor.<attributo>, por ejemplo
forecaster.regressor.estimators_.
—
Reply to this email directly, view it on GitHub
<#543 (comment)>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AC63YQZIL4WTKWDGWCTHBGLX4CHT3ANCNFSM6AAAAAA4YYYSUI>
.
You are receiving this because you were mentioned.Message ID:
***@***.***>
from skforecast.
Related Issues (20)
- `ForecasterAutoreg` fails to fit when `exog` do not have string column names HOT 1
- `ForecasterAutoreg` fails to fit when index do not start from 0 HOT 3
- How to use it with planning input or other forecast as a guide? HOT 5
- grid_search_sarimax stuck without any progress HOT 1
- Back testing HOT 1
- Backrest and hyper parameter tuning HOT 1
- Just a question about probabilistic forecasting HOT 3
- Naming convention for backtesting methods HOT 2
- Feature request regarding time series with different lengths HOT 5
- bayesian_search_forecaster (Optuna) & Saving/Resuming Study with RDB Backend HOT 1
- Feature request: Recursive multistep multivariate forecasting and direct multistep multi-series forecasting HOT 4
- A single model multivariate forecaster HOT 3
- Issue saving ForecasterSarimax object HOT 4
- Custom predictors are inefficient for window features HOT 1
- grid_search_sarimax takes a very long time to run HOT 1
- Feature request: Add ability to skip steps in backtesting HOT 6
- Bad error handling when Index is neither RangeIndex nor DateIndex HOT 3
- Why do i have faster runtime when using more frequent refits HOT 2
- syntax error !! HOT 1
- Feature request: Custom predictors for multivariate forecasting
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from skforecast.