"Run the following command to install the GAMA library with Pip:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bded45a9",
"metadata": {},
"outputs": [],
"source": [
"# pip install gama"
]
},
{
"cell_type": "markdown",
"id": "192cbbad",
"metadata": {},
"source": [
"Alternatively, the source code can be downloaded from the following Github repository:\n",
"\n",
"https://github.com/openml-labs/gama"
]
},
{
"cell_type": "markdown",
"id": "ce52d2cf",
"metadata": {},
"source": [
"## Accessing the Software"
]
},
{
"cell_type": "markdown",
"id": "ebfccd27",
"metadata": {},
"source": [
"To access the GAMA library after it is installed, run the following Python import command:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "00a14ad0",
"metadata": {},
"outputs": [],
"source": [
"import gama"
]
},
{
"cell_type": "markdown",
"id": "ad7f01c2",
"metadata": {},
"source": [
"## Overview"
]
},
{
"cell_type": "markdown",
"id": "bedce649",
"metadata": {},
"source": [
"GAMA is an AutoML package that generates optimized machine learning pipelines, given specific input data and resource constraints. To simplify intensive labor work of selecting the correct algorithm, all the user needs to do is supply the data, and GAMA will automatically try to find a good machine learning pipeline by considering data preprocessing steps, machine learning algorithm, and hyperparameter configurations. GAMA can also combine multiple tuned machine learning pipelines together into an ensemble, which can help model performance. GAMA is currently restricted to classification and regression problems on tabular data. <br><br>\n",
"\n",
"For more information about GAMA, see the link below:\n",
"In addition to its general use AutoML functionality, GAMA aims to serve AutoML researchers as well. During the optimization process, GAMA keeps an extensive log of progress made. Using this log, insight can be obtained on the behaviour of the search procedure. For example, it can produce a graph that shows pipeline fitness over time: graph of fitness over time"
]
},
{
"cell_type": "markdown",
"id": "5b3d0114",
"metadata": {},
"source": [
"## Code Examples"
]
},
{
"cell_type": "markdown",
"id": "6c9141bd",
"metadata": {},
"source": [
"For detailed API documentation see the following link:\n",
"GAMA generates optimized pipelines given specific input data and resource constraints. A machine learning pipeline contains data preprocessing (e.g. PCA, normalization) as well as a machine learning algorithm (e.g. Logistic Regression, Random Forests), with fine-tuned hyperparameter settings (e.g. number of trees in a Random Forest).\n",
"\n",
"GAMA can also combine multiple tuned machine learning pipelines together into an ensemble, which should help model performance. GAMA is currently restricted to classification and regression."
Run the following command to install the GAMA library with Pip:
%% Cell type:code id:bded45a9 tags:
``` python
# pip install gama
```
%% Cell type:markdown id:192cbbad tags:
Alternatively, the source code can be downloaded from the following Github repository:
https://github.com/openml-labs/gama
%% Cell type:markdown id:ce52d2cf tags:
## Accessing the Software
%% Cell type:markdown id:ebfccd27 tags:
To access the GAMA library after it is installed, run the following Python import command:
%% Cell type:code id:00a14ad0 tags:
``` python
importgama
```
%% Cell type:markdown id:ad7f01c2 tags:
## Overview
%% Cell type:markdown id:bedce649 tags:
GAMA is an AutoML package that generates optimized machine learning pipelines, given specific input data and resource constraints. To simplify intensive labor work of selecting the correct algorithm, all the user needs to do is supply the data, and GAMA will automatically try to find a good machine learning pipeline by considering data preprocessing steps, machine learning algorithm, and hyperparameter configurations. GAMA can also combine multiple tuned machine learning pipelines together into an ensemble, which can help model performance. GAMA is currently restricted to classification and regression problems on tabular data. <br><br>
For more information about GAMA, see the link below:
In addition to its general use AutoML functionality, GAMA aims to serve AutoML researchers as well. During the optimization process, GAMA keeps an extensive log of progress made. Using this log, insight can be obtained on the behaviour of the search procedure. For example, it can produce a graph that shows pipeline fitness over time: graph of fitness over time
%% Cell type:markdown id:5b3d0114 tags:
## Code Examples
%% Cell type:markdown id:6c9141bd tags:
For detailed API documentation see the following link:
GAMA generates optimized pipelines given specific input data and resource constraints. A machine learning pipeline contains data preprocessing (e.g. PCA, normalization) as well as a machine learning algorithm (e.g. Logistic Regression, Random Forests), with fine-tuned hyperparameter settings (e.g. number of trees in a Random Forest).
GAMA can also combine multiple tuned machine learning pipelines together into an ensemble, which should help model performance. GAMA is currently restricted to classification and regression.
