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<li><a class="reference internal" href="#">Basic Usage</a><ul>
<li><a class="reference internal" href="#connecting-to-the-openml-server">Connecting to the OpenML server</a></li>
<li><a class="reference internal" href="#key-concepts">Key concepts</a></li>
<li><a class="reference internal" href="#working-with-tasks">Working with tasks</a><ul>
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<div class="section" id="basic-usage">
<span id="usage"></span><h1>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to this headline">¶</a></h1>
<p>This document will guide you through the most important functions and classes
in the OpenML Python API. Throughout this document, we will use
<a class="reference external" href="http://pandas.pydata.org/">pandas</a> to format and filter tables.</p>
<div class="section" id="connecting-to-the-openml-server">
<h2>Connecting to the OpenML server<a class="headerlink" href="#connecting-to-the-openml-server" title="Permalink to this headline">¶</a></h2>
<p>The OpenML server can only be accessed by users who have signed up on the OpenML
platform. If you don’t have an account yet,
<a class="reference external" href="http://openml.org/register">sign up now</a>. You will receive an API key, which
will authenticate you to the server and allow you to download and upload
datasets, tasks, runs and flows. There are two ways of providing the API key
to the OpenML API package. The first option is to specify the API key
programmatically after loading the package:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">openml</span>
<span class="gp">>>> </span><span class="n">apikey</span> <span class="o">=</span> <span class="s1">'Your API key'</span>
<span class="gp">>>> </span><span class="n">openml</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">apikey</span> <span class="o">=</span> <span class="n">apikey</span>
</pre></div>
</div>
<p>The second option is to create a config file:</p>
<div class="code bash highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">apikey</span> <span class="o">=</span> <span class="n">qxlfpbeaudtprb23985hcqlfoebairtd</span>
</pre></div>
</div>
<p>The config file must be in the directory <code class="code bash docutils literal notranslate"><span class="pre">~/.openml/config</span></code> and
exist prior to importing the openml module.</p>
<p>When downloading datasets, tasks, runs and flows, they will be cached to
retrieve them without calling the server later. As with the API key, the cache
directory can be either specified through the API or through the config file:</p>
<p>API:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">os</span>
<span class="gp">>>> </span><span class="n">openml</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">cache_directory</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">expanduser</span><span class="p">(</span><span class="s1">'~/.openml/cache'</span><span class="p">)</span>
</pre></div>
</div>
<p>Config file:</p>
<div class="code bash highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cachedir</span> <span class="o">=</span> <span class="s1">'~/.openml/cache'</span>
</pre></div>
</div>
</div>
<div class="section" id="key-concepts">
<h2>Key concepts<a class="headerlink" href="#key-concepts" title="Permalink to this headline">¶</a></h2>
<p>OpenML contains several key concepts which it needs to make machine learning
research shareable. A machine learning experiment consists of one or several
<strong>runs</strong>, which describe the performance of an algorithm (called a <strong>flow</strong> in
OpenML), its hyperparameter settings (called a <strong>setup</strong>) on a <strong>task</strong>. A
<strong>Task</strong> is the combination of a <strong>dataset</strong>, a split and an evaluation
metric. In this user guide we will go through listing and exploring existing
<strong>tasks</strong> to actually running machine learning algorithms on them. In a further
user guide we will examine how to search through <strong>datasets</strong> in order to curate
a list of <strong>tasks</strong>.</p>
</div>
<div class="section" id="working-with-tasks">
<h2>Working with tasks<a class="headerlink" href="#working-with-tasks" title="Permalink to this headline">¶</a></h2>
<p>You can think of a task as an experimentation protocol, describing how to apply
a machine learning model to a dataset in a way that it is comparable with the
results of others (more on how to do that further down).Tasks are containers,
defining which dataset to use, what kind of task we’re solving (regression,
classification, clustering, etc…) and which column to predict. Furthermore,
it also describes how to split the dataset into a train and test set, whether
to use several disjoint train and test splits (cross-validation) and whether
this should be repeated several times. Also, the task defines a target metric
for which a flow should be optimized.</p>
<p>Tasks are identified by IDs and can be accessed in two different ways:</p>
<ol class="arabic simple">
<li>In a list providing basic information on all tasks available on OpenML.
This function will not download the actual tasks, but will instead download
meta data that can be used to filter the tasks and retrieve a set of IDs.
We can filter this list, for example, we can only list tasks having a special
tag or only tasks for a specific target such as <em>supervised classification</em>.</li>
<li>A single task by its ID. It contains all meta information, the target metric,
the splits and an iterator which can be used to access the splits in a
useful manner.</li>
</ol>
<p>You can also read more about tasks in the <a class="reference external" href="http://www.openml.org/guide">OpenML guide</a>.</p>
<div class="section" id="listing-tasks">
<h3>Listing tasks<a class="headerlink" href="#listing-tasks" title="Permalink to this headline">¶</a></h3>
<p>So far, this package only supports <em>supervised classification</em> tasks (task
type <code class="code python docutils literal notranslate"><span class="literal number integer"><span class="pre">1</span></span></code>). Therefore, well will start by simply listing all these tasks:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tasks</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">tasks</span><span class="o">.</span><span class="n">list_tasks</span><span class="p">(</span><span class="n">task_type_id</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<p><a class="reference internal" href="generated/openml.tasks.list_tasks.html#openml.tasks.list_tasks" title="openml.tasks.list_tasks"><code class="xref py py-meth docutils literal notranslate"><span class="pre">openml.tasks.list_tasks()</span></code></a> returns a dictionary of dictionaries, we convert
it into a
<a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html">pandas dataframe</a>
to have better visualization and easier access:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="gp">>>> </span><span class="n">tasks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">tasks</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">tasks</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="go">Index(['tid', 'ttid', 'did', 'name', 'task_type', 'status',</span>
<span class="go"> 'estimation_procedure', 'evaluation_measures', 'source_data',</span>
<span class="go"> 'target_feature', 'MajorityClassSize', 'MaxNominalAttDistinctValues',</span>
<span class="go"> 'MinorityClassSize', 'NumberOfClasses', 'NumberOfFeatures',</span>
<span class="go"> 'NumberOfInstances', 'NumberOfInstancesWithMissingValues',</span>
<span class="go"> 'NumberOfMissingValues', 'NumberOfNumericFeatures',</span>
<span class="go"> 'NumberOfSymbolicFeatures', 'cost_matrix'],</span>
<span class="go"> dtype='object')</span>
</pre></div>
</div>
<p>We can filter the list of tasks to only contain datasets with more than
500 samples, but less than 1000 samples:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">filtered_tasks</span> <span class="o">=</span> <span class="n">tasks</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">'NumberOfInstances > 500 and NumberOfInstances < 1000'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">filtered_tasks</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
<span class="go">[2, 11, 15, 29, 37, 41, 49, 53, ..., 146597, 146600, 146605]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">filtered_tasks</span><span class="p">))</span>
<span class="go">210</span>
</pre></div>
</div>
<p>Then, we can further restrict the tasks to all have the same resampling
strategy:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">filtered_tasks</span> <span class="o">=</span> <span class="n">filtered_tasks</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="s1">'estimation_procedure == "10-fold Crossvalidation"'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">filtered_tasks</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
<span class="go">[2, 11, 15, 29, 37, 41, 49, 53, ..., 146231, 146238, 146241]</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">filtered_tasks</span><span class="p">))</span>
<span class="go">107</span>
</pre></div>
</div>
<p>Resampling strategies can be found on the <a class="reference external" href="http://www.openml.org/search?type=measure&q=estimation%20procedure">OpenML Website</a>.</p>
<p>Similar to listing tasks by task type, we can list tasks by tags:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tasks</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">tasks</span><span class="o">.</span><span class="n">list_tasks</span><span class="p">(</span><span class="n">tag</span><span class="o">=</span><span class="s1">'OpenML100'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tasks</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">tasks</span><span class="p">,</span> <span class="n">orient</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span>
</pre></div>
</div>
<p><em>OpenML 100</em> is a curated list of 100 tasks to start using OpenML. They are all
supervised classification tasks with more than 500 instances and less than 50000
instances per task. To make things easier, the tasks do not contain highly
unbalanced data and sparse data. However, the tasks include missing values and
categorical features. You can find out more about the <em>OpenML 100</em> on
<a class="reference external" href="https://www.openml.org/guide/benchmark">the OpenML benchmarking page</a>.</p>
<p>Finally, it is also possible to list all tasks on OpenML with:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tasks</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">tasks</span><span class="o">.</span><span class="n">list_tasks</span><span class="p">()</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tasks</span><span class="p">))</span>
<span class="go">46067</span>
</pre></div>
</div>
</div>
<div class="section" id="downloading-tasks">
<h3>Downloading tasks<a class="headerlink" href="#downloading-tasks" title="Permalink to this headline">¶</a></h3>
<p>We provide two functions to download tasks, one which downloads only a single
task by its ID, and one which takes a list of IDs and downloads all of these
tasks:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">task_id</span> <span class="o">=</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">task</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">tasks</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span><span class="n">task_id</span><span class="p">)</span>
</pre></div>
</div>
<p>Properties of the task are stored as member variables:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">pprint</span> <span class="k">import</span> <span class="n">pprint</span>
<span class="gp">>>> </span><span class="n">pprint</span><span class="p">(</span><span class="nb">vars</span><span class="p">(</span><span class="n">task</span><span class="p">))</span>
<span class="go">{'class_labels': ['1', '2', '3', '4', '5', 'U'],</span>
<span class="go"> 'cost_matrix': None,</span>
<span class="go"> 'dataset_id': 2,</span>
<span class="go"> 'estimation_parameters': {'number_folds': '10',</span>
<span class="go"> 'number_repeats': '1',</span>
<span class="go"> 'percentage': '',</span>
<span class="go"> 'stratified_sampling': 'true'},</span>
<span class="go"> 'estimation_procedure': {'data_splits_url': 'https://www.openml.org/api_splits/get/2/Task_2_splits.arff',</span>
<span class="go"> 'parameters': {'number_folds': '10',</span>
<span class="go"> 'number_repeats': '1',</span>
<span class="go"> 'percentage': '',</span>
<span class="go"> 'stratified_sampling': 'true'},</span>
<span class="go"> 'type': 'crossvalidation'},</span>
<span class="go"> 'evaluation_measure': 'predictive_accuracy',</span>
<span class="go"> 'split': None,</span>
<span class="go"> 'target_name': 'class',</span>
<span class="go"> 'task_id': 2,</span>
<span class="go"> 'task_type': 'Supervised Classification',</span>
<span class="go"> 'task_type_id': 1}</span>
</pre></div>
</div>
<p>And:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ids</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">29</span><span class="p">,</span> <span class="mi">37</span><span class="p">,</span> <span class="mi">41</span><span class="p">,</span> <span class="mi">49</span><span class="p">,</span> <span class="mi">53</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">tasks</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">tasks</span><span class="o">.</span><span class="n">get_tasks</span><span class="p">(</span><span class="n">ids</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pprint</span><span class="p">(</span><span class="n">tasks</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="creating-runs">
<h2>Creating runs<a class="headerlink" href="#creating-runs" title="Permalink to this headline">¶</a></h2>
<p>In order to upload and share results of running a machine learning algorithm
on a task, we need to create an <a class="reference internal" href="generated/openml.OpenMLRun.html#openml.OpenMLRun" title="openml.OpenMLRun"><code class="xref py py-class docutils literal notranslate"><span class="pre">OpenMLRun</span></code></a>. A run object can
be created by running a <a class="reference internal" href="generated/openml.OpenMLFlow.html#openml.OpenMLFlow" title="openml.OpenMLFlow"><code class="xref py py-class docutils literal notranslate"><span class="pre">OpenMLFlow</span></code></a> or a scikit-learn compatible
model on a task. We will focus on the simpler example of running a
scikit-learn model.</p>
<p>Flows are descriptions of something runable which does the machine learning.
A flow contains all information to set up the necessary machine learning
library and its dependencies as well as all possible parameters.</p>
<p>A run is the outcome of running a flow on a task. It contains all parameter
settings for the flow, a setup string (most likely a command line call) and all
predictions of that run. When a run is uploaded to the server, the server
automatically calculates several metrics which can be used to compare the
performance of different flows to each other.</p>
<p>So far, the OpenML python connector works only with estimator objects following
the <a class="reference external" href="http://scikit-learn.org/dev/developers/contributing.html#apis-of-scikit-learn-objects">scikit-learn estimator API</a>.
Those can be directly run on a task, and a flow will automatically be created or
downloaded from the server if it already exists.</p>
<div class="section" id="running-a-model">
<h3>Running a model<a class="headerlink" href="#running-a-model" title="Permalink to this headline">¶</a></h3>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">RandomForestClassifier</span>
<span class="gp">>>> </span><span class="n">model</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">task</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">tasks</span><span class="o">.</span><span class="n">get_task</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">run</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">runs</span><span class="o">.</span><span class="n">run_model_on_task</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pprint</span><span class="p">(</span><span class="nb">vars</span><span class="p">(</span><span class="n">run</span><span class="p">),</span> <span class="n">depth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="go">{'data_content': [...],</span>
<span class="go"> 'dataset_id': 12,</span>
<span class="go"> 'error_message': None,</span>
<span class="go"> 'evaluations': None,</span>
<span class="go"> 'flow': None,</span>
<span class="go"> 'flow_id': 7257,</span>
<span class="go"> 'flow_name': None,</span>
<span class="go"> 'fold_evaluations': defaultdict(<function _run_task_get_arffcontent.<locals>.<lambda> at 0x7fb88981b9d8>,</span>
<span class="go"> {'predictive_accuracy': defaultdict(<class 'dict'>,</span>
<span class="go"> {0: {0: 0.94499999999999995,</span>
<span class="go"> 1: 0.94499999999999995,</span>
<span class="go"> 2: 0.94499999999999995,</span>
<span class="go"> 3: 0.96499999999999997,</span>
<span class="go"> 4: 0.92500000000000004,</span>
<span class="go"> 5: 0.96499999999999997,</span>
<span class="go"> 6: 0.94999999999999996,</span>
<span class="go"> 7: 0.96999999999999997,</span>
<span class="go"> 8: 0.93999999999999995,</span>
<span class="go"> 9: 0.95499999999999996}}),</span>
<span class="go"> 'usercpu_time_millis': defaultdict(<class 'dict'>,</span>
<span class="go"> {0: {0: 110.4880920000042,</span>
<span class="go"> 1: 105.7469440000034,</span>
<span class="go"> 2: 107.4153629999941,</span>
<span class="go"> 3: 105.1104170000059,</span>
<span class="go"> 4: 104.02388900000403,</span>
<span class="go"> 5: 105.17172800000196,</span>
<span class="go"> 6: 109.00792000001047,</span>
<span class="go"> 7: 107.49670599999206,</span>
<span class="go"> 8: 107.34138000000115,</span>
<span class="go"> 9: 104.78881499999915}}),</span>
<span class="go"> 'usercpu_time_millis_testing': defaultdict(<class 'dict'>,</span>
<span class="go"> {0: {0: 3.6470320000034917,</span>
<span class="go"> 1: 3.5307810000020368,</span>
<span class="go"> 2: 3.5432540000002177,</span>
<span class="go"> 3: 3.5460690000022055,</span>
<span class="go"> 4: 3.5634600000022942,</span>
<span class="go"> 5: 3.906016000001955,</span>
<span class="go"> 6: 3.6680000000046675,</span>
<span class="go"> 7: 3.643865999997331,</span>
<span class="go"> 8: 3.4515420000005292,</span>
<span class="go"> 9: 3.461469000001216}}),</span>
<span class="go"> 'usercpu_time_millis_training': defaultdict(<class 'dict'>,</span>
<span class="go"> {0: {0: 106.84106000000071,</span>
<span class="go"> 1: 102.21616300000136,</span>
<span class="go"> 2: 103.87210899999388,</span>
<span class="go"> 3: 101.56434800000369,</span>
<span class="go"> 4: 100.46042900000174,</span>
<span class="go"> 5: 101.26571200000001,</span>
<span class="go"> 6: 105.3399200000058,</span>
<span class="go"> 7: 103.85283999999473,</span>
<span class="go"> 8: 103.88983800000062,</span>
<span class="go"> 9: 101.32734599999793}})}),</span>
<span class="go"> 'model': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',</span>
<span class="go"> max_depth=None, max_features='auto', max_leaf_nodes=None,</span>
<span class="go"> min_impurity_split=1e-07, min_samples_leaf=1,</span>
<span class="go"> min_samples_split=2, min_weight_fraction_leaf=0.0,</span>
<span class="go"> n_estimators=10, n_jobs=1, oob_score=False, random_state=43934,</span>
<span class="go"> verbose=0, warm_start=False),</span>
<span class="go"> 'output_files': None,</span>
<span class="go"> 'parameter_settings': [...],</span>
<span class="go"> 'predictions_url': None,</span>
<span class="go"> 'run_id': None,</span>
<span class="go"> 'sample_evaluations': None,</span>
<span class="go"> 'setup_id': None,</span>
<span class="go"> 'setup_string': None,</span>
<span class="go"> 'tags': [...],</span>
<span class="go"> 'task': None,</span>
<span class="go"> 'task_evaluation_measure': None,</span>
<span class="go"> 'task_id': 12,</span>
<span class="go"> 'task_type': None,</span>
<span class="go"> 'trace_attributes': None,</span>
<span class="go"> 'trace_content': None,</span>
<span class="go"> 'uploader': None,</span>
<span class="go"> 'uploader_name': None}</span>
</pre></div>
</div>
<p>So far the run is only available locally. By calling the publish function, the
run is send to the OpenML server:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">run</span><span class="o">.</span><span class="n">publish</span><span class="p">()</span>
<span class="go"><openml.runs.run.OpenMLRun at 0x7fb8953d72e8></span>
</pre></div>
</div>
<p>We can now also inspect the flow object which was automatically created:</p>
<div class="code python highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">flow</span> <span class="o">=</span> <span class="n">openml</span><span class="o">.</span><span class="n">flows</span><span class="o">.</span><span class="n">get_flow</span><span class="p">(</span><span class="n">run</span><span class="o">.</span><span class="n">flow_id</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pprint</span><span class="p">(</span><span class="nb">vars</span><span class="p">(</span><span class="n">flow</span><span class="p">),</span> <span class="n">depth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">{'binary_format': None,</span>
<span class="go"> 'binary_md5': None,</span>
<span class="go"> 'binary_url': None,</span>
<span class="go"> 'class_name': 'sklearn.ensemble.forest.RandomForestClassifier',</span>
<span class="go"> 'components': OrderedDict(),</span>
<span class="go"> 'custom_name': None,</span>
<span class="go"> 'dependencies': 'sklearn==0.18.2\nnumpy>=1.6.1\nscipy>=0.9',</span>
<span class="go"> 'description': 'Automatically created scikit-learn flow.',</span>
<span class="go"> 'external_version': 'openml==0.6.0,sklearn==0.18.2',</span>
<span class="go"> 'flow_id': 7257,</span>
<span class="go"> 'language': 'English',</span>
<span class="go"> 'model': RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',</span>
<span class="go"> max_depth=None, max_features='auto', max_leaf_nodes=None,</span>
<span class="go"> min_impurity_split=1e-07, min_samples_leaf=1,</span>
<span class="go"> min_samples_split=2, min_weight_fraction_leaf=0.0,</span>
<span class="go"> n_estimators=10, n_jobs=1, oob_score=False, random_state=None,</span>
<span class="go"> verbose=0, warm_start=False),</span>
<span class="go"> 'name': 'sklearn.ensemble.forest.RandomForestClassifier',</span>
<span class="go"> 'parameters': OrderedDict([...]),</span>
<span class="go"> 'parameters_meta_info': OrderedDict([...]),</span>
<span class="go"> 'tags': [...],</span>
<span class="go"> 'upload_date': '2017-10-09T10:20:40',</span>
<span class="go"> 'uploader': '1159',</span>
<span class="go"> 'version': '29'}</span>
</pre></div>
</div>
</div>
<div class="section" id="advanced-topics">
<h3>Advanced topics<a class="headerlink" href="#advanced-topics" title="Permalink to this headline">¶</a></h3>
<p>We are working on tutorials for the following topics:</p>
<ul class="simple">
<li>Querying datasets</li>
<li>Uploading datasets</li>
<li>Creating tasks</li>
<li>Working offline</li>
<li>Analyzing large amounts of results</li>
</ul>
</div>
</div>
</div>
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