Decision Tree Viewer (D3 and Sklearn)

boston.json

{"error": 42716.2954, "samples": 506, "value": [22.532806324110698], "label": "RM <= 6.94", "type": "split", "children": [{"error": 17317.3210, "samples": 430, "value": [19.93372093023257], "label": "LSTAT <= 14.40", "type": "split", "children": [{"error": 6632.2175, "samples": 255, "value": [23.349803921568636], "label": "DIS <= 1.38", "type": "split", "children": [{"error": 390.7280, "samples": 5, "value": [45.58], "label": "CRIM <= 10.59", "type": "split", "children": [{"error": 0.0000, "samples": 4, "value": [50.0], "label": "Leaf - 4", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [27.9], "label": "Leaf - 5", "type": "leaf"}]}, {"error": 3721.1632, "samples": 250, "value": [22.90520000000001], "label": "RM <= 6.54", "type": "split", "children": [{"error": 1636.0675, "samples": 195, "value": [21.629743589743576], "label": "LSTAT <= 7.57", "type": "split", "children": [{"error": 129.6307, "samples": 43, "value": [23.969767441860473], "label": "TAX <= 222.50", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [28.7], "label": "Leaf - 9", "type": "leaf"}, {"error": 106.7229, "samples": 42, "value": [23.85714285714286], "label": "Leaf - 10", "type": "leaf"}]}, {"error": 1204.3720, "samples": 152, "value": [20.967763157894723], "label": "TAX <= 208.00", "type": "split", "children": [{"error": 161.6000, "samples": 5, "value": [26.9], "label": "Leaf - 12", "type": "leaf"}, {"error": 860.8299, "samples": 147, "value": [20.765986394557814], "label": "Leaf - 13", "type": "leaf"}]}]}, {"error": 643.1691, "samples": 55, "value": [27.427272727272726], "label": "TAX <= 269.00", "type": "split", "children": [{"error": 91.4612, "samples": 17, "value": [30.24117647058823], "label": "PTRATIO <= 17.85", "type": "split", "children": [{"error": 26.9890, "samples": 10, "value": [31.71], "label": "Leaf - 16", "type": "leaf"}, {"error": 12.0771, "samples": 7, "value": [28.142857142857142], "label": "Leaf - 17", "type": "leaf"}]}, {"error": 356.8821, "samples": 38, "value": [26.16842105263158], "label": "NOX <= 0.53", "type": "split", "children": [{"error": 232.6986, "samples": 29, "value": [27.006896551724143], "label": "Leaf - 19", "type": "leaf"}, {"error": 38.1000, "samples": 9, "value": [23.466666666666665], "label": "Leaf - 20", "type": "leaf"}]}]}]}]}, {"error": 3373.2512, "samples": 175, "value": [14.955999999999996], "label": "NOX <= 0.61", "type": "split", "children": [{"error": 833.2624, "samples": 68, "value": [18.123529411764697], "label": "CRIM <= 0.55", "type": "split", "children": [{"error": 272.4123, "samples": 39, "value": [19.738461538461536], "label": "AGE <= 60.55", "type": "split", "children": [{"error": 22.5743, "samples": 7, "value": [22.071428571428573], "label": "NOX <= 0.46", "type": "split", "children": [{"error": 0.9800, "samples": 2, "value": [19.6], "label": "Leaf - 25", "type": "leaf"}, {"error": 4.4920, "samples": 5, "value": [23.060000000000002], "label": "Leaf - 26", "type": "leaf"}]}, {"error": 203.4047, "samples": 32, "value": [19.228125], "label": "LSTAT <= 24.69", "type": "split", "children": [{"error": 150.4386, "samples": 28, "value": [19.692857142857147], "label": "Leaf - 28", "type": "leaf"}, {"error": 4.5875, "samples": 4, "value": [15.975000000000001], "label": "Leaf - 29", "type": "leaf"}]}]}, {"error": 322.3524, "samples": 29, "value": [15.951724137931038], "label": "RM <= 6.84", "type": "split", "children": [{"error": 184.2268, "samples": 28, "value": [15.539285714285716], "label": "B <= 26.72", "type": "split", "children": [{"error": 1.1250, "samples": 2, "value": [10.95], "label": "Leaf - 32", "type": "leaf"}, {"error": 137.7385, "samples": 26, "value": [15.892307692307696], "label": "Leaf - 33", "type": "leaf"}]}, {"error": 0.0000, "samples": 1, "value": [27.5], "label": "Leaf - 34", "type": "leaf"}]}]}, {"error": 1424.1422, "samples": 107, "value": [12.942990654205609], "label": "LSTAT <= 19.65", "type": "split", "children": [{"error": 316.3804, "samples": 51, "value": [15.480392156862749], "label": "CRIM <= 12.22", "type": "split", "children": [{"error": 232.6349, "samples": 47, "value": [15.842553191489367], "label": "CRIM <= 5.77", "type": "split", "children": [{"error": 132.1443, "samples": 28, "value": [16.535714285714285], "label": "Leaf - 38", "type": "leaf"}, {"error": 67.2116, "samples": 19, "value": [14.821052631578949], "label": "Leaf - 39", "type": "leaf"}]}, {"error": 5.1475, "samples": 4, "value": [11.225], "label": "CRIM <= 14.17", "type": "split", "children": [{"error": 0.5000, "samples": 2, "value": [12.2], "label": "Leaf - 41", "type": "leaf"}, {"error": 0.8450, "samples": 2, "value": [10.25], "label": "Leaf - 42", "type": "leaf"}]}]}, {"error": 480.3621, "samples": 56, "value": [10.632142857142854], "label": "TAX <= 551.50", "type": "split", "children": [{"error": 23.5290, "samples": 10, "value": [14.41], "label": "DIS <= 1.38", "type": "split", "children": [{"error": 1.2800, "samples": 2, "value": [12.600000000000001], "label": "Leaf - 45", "type": "leaf"}, {"error": 14.0588, "samples": 8, "value": [14.8625], "label": "Leaf - 46", "type": "leaf"}]}, {"error": 283.0846, "samples": 46, "value": [9.81086956521739], "label": "DIS <= 1.41", "type": "split", "children": [{"error": 11.0971, "samples": 7, "value": [12.857142857142858], "label": "Leaf - 48", "type": "leaf"}, {"error": 195.3697, "samples": 39, "value": [9.264102564102567], "label": "Leaf - 49", "type": "leaf"}]}]}]}]}]}, {"error": 6059.4193, "samples": 76, "value": [37.23815789473684], "label": "RM <= 7.44", "type": "split", "children": [{"error": 1899.6122, "samples": 46, "value": [32.11304347826087], "label": "CRIM <= 7.39", "type": "split", "children": [{"error": 864.7674, "samples": 43, "value": [33.348837209302324], "label": "DIS <= 1.89", "type": "split", "children": [{"error": 37.8450, "samples": 2, "value": [45.65], "label": "INDUS <= 18.84", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [50.0], "label": "Leaf - 54", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [41.3], "label": "Leaf - 55", "type": "leaf"}]}, {"error": 509.5224, "samples": 41, "value": [32.74878048780488], "label": "NOX <= 0.49", "type": "split", "children": [{"error": 135.3867, "samples": 27, "value": [34.15555555555556], "label": "AGE <= 11.95", "type": "split", "children": [{"error": 0.1800, "samples": 2, "value": [29.3], "label": "Leaf - 58", "type": "leaf"}, {"error": 84.2816, "samples": 25, "value": [34.544000000000004], "label": "Leaf - 59", "type": "leaf"}]}, {"error": 217.6521, "samples": 14, "value": [30.03571428571428], "label": "RM <= 7.12", "type": "split", "children": [{"error": 49.6286, "samples": 7, "value": [26.914285714285715], "label": "Leaf - 61", "type": "leaf"}, {"error": 31.6171, "samples": 7, "value": [33.15714285714286], "label": "Leaf - 62", "type": "leaf"}]}]}]}, {"error": 27.9200, "samples": 3, "value": [14.4], "label": "RM <= 7.14", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [10.4], "label": "Leaf - 64", "type": "leaf"}, {"error": 3.9200, "samples": 2, "value": [16.4], "label": "CRIM <= 13.93", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [17.8], "label": "Leaf - 66", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [15.0], "label": "Leaf - 67", "type": "leaf"}]}]}]}, {"error": 1098.8497, "samples": 30, "value": [45.09666666666668], "label": "B <= 361.92", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [21.9], "label": "Leaf - 69", "type": "leaf"}, {"error": 542.2097, "samples": 29, "value": [45.896551724137936], "label": "PTRATIO <= 14.80", "type": "split", "children": [{"error": 112.3800, "samples": 14, "value": [48.300000000000004], "label": "RM <= 7.71", "type": "split", "children": [{"error": 37.8475, "samples": 4, "value": [44.725], "label": "CRIM <= 1.00", "type": "split", "children": [{"error": 0.7467, "samples": 3, "value": [42.96666666666667], "label": "Leaf - 73", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [50.0], "label": "Leaf - 74", "type": "leaf"}]}, {"error": 2.9610, "samples": 10, "value": [49.730000000000004], "label": "LSTAT <= 3.75", "type": "split", "children": [{"error": 0.0000, "samples": 6, "value": [50.0], "label": "Leaf - 76", "type": "leaf"}, {"error": 1.8675, "samples": 4, "value": [49.325], "label": "Leaf - 77", "type": "leaf"}]}]}, {"error": 273.4773, "samples": 15, "value": [43.653333333333336], "label": "B <= 385.48", "type": "split", "children": [{"error": 16.4920, "samples": 5, "value": [47.160000000000004], "label": "CRIM <= 0.32", "type": "split", "children": [{"error": 1.8467, "samples": 3, "value": [45.833333333333336], "label": "Leaf - 80", "type": "leaf"}, {"error": 1.4450, "samples": 2, "value": [49.15], "label": "Leaf - 81", "type": "leaf"}]}, {"error": 164.7600, "samples": 10, "value": [41.9], "label": "CRIM <= 0.06", "type": "split", "children": [{"error": 19.7067, "samples": 3, "value": [46.46666666666667], "label": "Leaf - 83", "type": "leaf"}, {"error": 55.6771, "samples": 7, "value": [39.94285714285714], "label": "Leaf - 84", "type": "leaf"}]}]}]}]}]}]}

export.py

import numpy as np

from sklearn.tree import _tree


def export_json(decision_tree, out_file=None, feature_names=None):
    """Export a decision tree in JSON format.

    This function generates a JSON representation of the decision tree,
    which is then written into `out_file`. Once exported, graphical renderings
    can be generated using, for example::

        $ dot -Tps tree.dot -o tree.ps      (PostScript format)
        $ dot -Tpng tree.dot -o tree.png    (PNG format)

    Parameters
    ----------
    decision_tree : decision tree classifier
        The decision tree to be exported to JSON.

    out : file object or string, optional (default=None)
        Handle or name of the output file.

    feature_names : list of strings, optional (default=None)
        Names of each of the features.

    Returns
    -------
    out_file : file object
        The file object to which the tree was exported.  The user is
        expected to `close()` this object when done with it.

    Examples
    --------
    >>> from sklearn.datasets import load_iris
    >>> from sklearn import tree

    >>> clf = tree.DecisionTreeClassifier()
    >>> iris = load_iris()

    >>> clf = clf.fit(iris.data, iris.target)
    >>> import tempfile
    >>> out_file = tree.export_json(clf, out_file=tempfile.TemporaryFile())
    >>> out_file.close()
    """
    import numpy as np

    from sklearn.tree import _tree

    def arr_to_py(arr):
        arr = arr.ravel()
        wrapper = float
        if np.issubdtype(arr.dtype, np.int):
            wrapper = int
        return map(wrapper, arr.tolist())


    def node_to_str(tree, node_id):
        node_repr = '"error": %.4f, "samples": %d, "value": %s' \
                    % (tree.init_error[node_id],
                       tree.n_samples[node_id],
                       arr_to_py(tree.value[node_id]))
        if tree.children_left[node_id] != _tree.TREE_LEAF:
            if feature_names is not None:
                feature = feature_names[tree.feature[node_id]]
            else:
                feature = "X[%s]" % tree.feature[node_id]

            label = '"label": "%s <= %.2f"' % (feature,
                                               tree.threshold[node_id])
            node_type = '"type": "split"'
        else:
            node_type = '"type": "leaf"'
            label = '"label": "Leaf - %d"' % node_id
        node_repr = ", ".join((node_repr, label, node_type))
        return node_repr

    def recurse(tree, node_id, parent=None):
        if node_id == _tree.TREE_LEAF:
            raise ValueError("Invalid node_id %s" % _tree.TREE_LEAF)

        left_child = tree.children_left[node_id]
        right_child = tree.children_right[node_id]

        # Open node with description
        out_file.write('{%s' % node_to_str(tree, node_id))

        # write children
        if left_child != _tree.TREE_LEAF:  # and right_child != _tree.TREE_LEAF
            out_file.write(', "children": [')
            recurse(tree, left_child, node_id)
            out_file.write(', ')
            recurse(tree, right_child, node_id)
            out_file.write(']')

        # close node
        out_file.write('}')

    if out_file is None:
        out_file = open("tree.json", "w")
    elif isinstance(out_file, basestring):
        out_file = open(out_file, "w")

    if isinstance(decision_tree, _tree.Tree):
        recurse(decision_tree, 0)
    else:
        recurse(decision_tree.tree_, 0)

    return out_file

index.html

<!DOCTYPE html>
<html>
  <head>
    <meta http-equiv="Content-Type" content="text/html;charset=utf-8"/>
    <script type="text/javascript" src="http://mbostock.github.com/d3/d3.js?2.9.0"></script>
    <style type="text/css">

body {
  font-family: "Helvetica Neue", Helvetica;
}

.hint {
  font-size: 12px;
  color: #999;
}

.node rect {
  cursor: pointer;
  fill: #fff;
  stroke-width: 1.5px;
}

.node text {
  font-size: 11px;
}

path.link {
  fill: none;
  stroke: #ccc;
}

    </style>
  </head>
  <body>
    <div id="body">
      <div id="footer">
        Decision Tree viewer
        <div class="hint">click to expand or collapse</div>
        <div id="menu">
          <select id="datasets"></select>
        </div>

      </div>
    </div>
    <script type="text/javascript">

var m = [20, 120, 20, 120],
    w = 1280 - m[1] - m[3],
    h = 800 - m[0] - m[2],
    i = 0,
    rect_width = 80,
    rect_height = 20,
    max_link_width = 20,
    min_link_width = 1.5,
    char_to_pxl = 6,
    root;

// Add datasets dropdown
d3.select("#datasets")
    .on("change", function() {
      if (this.value !== '-') {
        d3.json(this.value + ".json", load_dataset);
      }
    })
  .selectAll("option")
    .data([
      "-",
      "iris",
      "boston",
    ])
  .enter().append("option")
    .attr("value", String)
    .text(String);

var tree = d3.layout.tree()
    .size([h, w]);

var diagonal = d3.svg.diagonal()
    .projection(function(d) { return [d.x, d.y]; });

var vis = d3.select("#body").append("svg:svg")
    .attr("width", w + m[1] + m[3])
    .attr("height", h + m[0] + m[2] + 1000)
  .append("svg:g")
    .attr("transform", "translate(" + m[3] + "," + m[0] + ")");

// global scale for link width
var link_stoke_scale = d3.scale.linear();

var color_map = d3.scale.category10();

// stroke style of link - either color or function
var stroke_callback = "#ccc";

function load_dataset(json) {
  root = json;
  root.x0 = 0;
  root.y0 = 0;

  var n_samples = root.samples;
  var n_labels = root.value.length;

  if (n_labels >= 2) {
    stroke_callback = mix_colors;
  } else if (n_labels === 1) {
    stroke_callback = mean_interpolation(root);
  }

  link_stoke_scale = d3.scale.linear()
                             .domain([0, n_samples])
                             .range([min_link_width, max_link_width]);

  function toggleAll(d) {
    if (d && d.children) {
      d.children.forEach(toggleAll);
      toggle(d);
    }
  }

  // Initialize the display to show a few nodes.
  root.children.forEach(toggleAll);

  update(root);
}

function update(source) {
  var duration = d3.event && d3.event.altKey ? 5000 : 500;

  // Compute the new tree layout.
  var nodes = tree.nodes(root).reverse();

  // Normalize for fixed-depth.
  nodes.forEach(function(d) { d.y = d.depth * 180; });

  // Update the nodes…
  var node = vis.selectAll("g.node")
      .data(nodes, function(d) { return d.id || (d.id = ++i); });

  // Enter any new nodes at the parent's previous position.
  var nodeEnter = node.enter().append("svg:g")
      .attr("class", "node")
      .attr("transform", function(d) { return "translate(" + source.x0 + "," + source.y0 + ")"; })
      .on("click", function(d) { toggle(d); update(d); });

  nodeEnter.append("svg:rect")
      .attr("x", function(d) {
        var label = node_label(d);
        var text_len = label.length * char_to_pxl;
        var width = d3.max([rect_width, text_len])
        return -width / 2;
      })
      .attr("width", 1e-6)
      .attr("height", 1e-6)
      .attr("rx", function(d) { return d.type === "split" ? 2 : 0;})
      .attr("ry", function(d) { return d.type === "split" ? 2 : 0;})
      .style("stroke", function(d) { return d.type === "split" ? "steelblue" : "olivedrab";})
      .style("fill", function(d) { return d._children ? "lightsteelblue" : "#fff"; });

  nodeEnter.append("svg:text")
      .attr("dy", "12px")
      .attr("text-anchor", "middle")
      .text(node_label)
      .style("fill-opacity", 1e-6);

  // Transition nodes to their new position.
  var nodeUpdate = node.transition()
      .duration(duration)
      .attr("transform", function(d) { return "translate(" + d.x + "," + d.y + ")"; });

  nodeUpdate.select("rect")
      .attr("width", function(d) {
        var label = node_label(d);
        var text_len = label.length * char_to_pxl;
        var width = d3.max([rect_width, text_len])
        return width;
      })
      .attr("height", rect_height)
      .style("fill", function(d) { return d._children ? "lightsteelblue" : "#fff"; });

  nodeUpdate.select("text")
      .style("fill-opacity", 1);

  // Transition exiting nodes to the parent's new position.
  var nodeExit = node.exit().transition()
      .duration(duration)
      .attr("transform", function(d) { return "translate(" + source.x + "," + source.y + ")"; })
      .remove();

  nodeExit.select("rect")
      .attr("width", 1e-6)
      .attr("height", 1e-6);

  nodeExit.select("text")
      .style("fill-opacity", 1e-6);

  // Update the links
  var link = vis.selectAll("path.link")
      .data(tree.links(nodes), function(d) { return d.target.id; });

  // Enter any new links at the parent's previous position.
  link.enter().insert("svg:path", "g")
      .attr("class", "link")
      .attr("d", function(d) {
        var o = {x: source.x0, y: source.y0};
        return diagonal({source: o, target: o});
      })
      .transition()
      .duration(duration)
      .attr("d", diagonal)
      .style("stroke-width", function(d) {return link_stoke_scale(d.target.samples);})
      .style("stroke", stroke_callback);

  // Transition links to their new position.
  link.transition()
      .duration(duration)
      .attr("d", diagonal)
      .style("stroke-width", function(d) {return link_stoke_scale(d.target.samples);})
      .style("stroke", stroke_callback);

  // Transition exiting nodes to the parent's new position.
  link.exit().transition()
      .duration(duration)
      .attr("d", function(d) {
        var o = {x: source.x, y: source.y};
        return diagonal({source: o, target: o});
      })
      .remove();

  // Stash the old positions for transition.
  nodes.forEach(function(d) {
    d.x0 = d.x;
    d.y0 = d.y;
  });
}

// Toggle children.
function toggle(d) {
  if (d.children) {
    d._children = d.children;
    d.children = null;
  } else {
    d.children = d._children;
    d._children = null;
  }
}

// Node labels
function node_label(d) {
  if (d.type === "leaf") {
    // leaf
    var formatter = d3.format(".2f");
    var vals = [];
    d.value.forEach(function(v) {
        vals.push(formatter(v));
    });
    return "[" + vals.join(", ") + "]";
  } else {
    // split node
    return d.label;
  }
}

/**
 * Mixes colors according to the relative frequency of classes.
 */
function mix_colors(d) {
  var value = d.target.value;
  var sum = d3.sum(value);
  var col = d3.rgb(0, 0, 0);
  value.forEach(function(val, i) {
    var label_color = d3.rgb(color_map(i));
    var mix_coef = val / sum;
    col.r += mix_coef * label_color.r;
    col.g += mix_coef * label_color.g;
    col.b += mix_coef * label_color.b;
  });
  return col;
}


/**
 * A linear interpolator for value[0].
 *
 * Useful for link coloring in regression trees.
 */
function mean_interpolation(root) {

  var max = 1e-9,
      min = 1e9;

  function recurse(node) {
    if (node.value[0] > max) {
      max = node.value[0];
    }

    if (node.value[0] < min) {
      min = node.value[0];
    }

    if (node.children) {
      node.children.forEach(recurse);
    }
  }
  recurse(root);

  var scale = d3.scale.linear().domain([min, max])
                               .range(["#2166AC","#B2182B"]);

  function interpolator(d) {
    return scale(d.target.value[0]);
  }

  return interpolator;
}

    </script>
  </body>
</html>

iris.json

{"error": 0.6667, "samples": 150, "value": [50.0, 50.0, 50.0], "label": "X[2] <= 2.45", "type": "split", "children": [{"error": 0.0000, "samples": 50, "value": [50.0, 0.0, 0.0], "label": "Leaf - 1", "type": "leaf"}, {"error": 0.5000, "samples": 100, "value": [0.0, 50.0, 50.0], "label": "X[3] <= 1.75", "type": "split", "children": [{"error": 0.1680, "samples": 54, "value": [0.0, 49.0, 5.0], "label": "X[2] <= 4.95", "type": "split", "children": [{"error": 0.0408, "samples": 48, "value": [0.0, 47.0, 1.0], "label": "X[3] <= 1.65", "type": "split", "children": [{"error": 0.0000, "samples": 47, "value": [0.0, 47.0, 0.0], "label": "Leaf - 5", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [0.0, 0.0, 1.0], "label": "Leaf - 6", "type": "leaf"}]}, {"error": 0.4444, "samples": 6, "value": [0.0, 2.0, 4.0], "label": "X[3] <= 1.55", "type": "split", "children": [{"error": 0.0000, "samples": 3, "value": [0.0, 0.0, 3.0], "label": "Leaf - 8", "type": "leaf"}, {"error": 0.4444, "samples": 3, "value": [0.0, 2.0, 1.0], "label": "X[0] <= 6.95", "type": "split", "children": [{"error": 0.0000, "samples": 2, "value": [0.0, 2.0, 0.0], "label": "Leaf - 10", "type": "leaf"}, {"error": 0.0000, "samples": 1, "value": [0.0, 0.0, 1.0], "label": "Leaf - 11", "type": "leaf"}]}]}]}, {"error": 0.0425, "samples": 46, "value": [0.0, 1.0, 45.0], "label": "X[2] <= 4.85", "type": "split", "children": [{"error": 0.4444, "samples": 3, "value": [0.0, 1.0, 2.0], "label": "X[0] <= 5.95", "type": "split", "children": [{"error": 0.0000, "samples": 1, "value": [0.0, 1.0, 0.0], "label": "Leaf - 14", "type": "leaf"}, {"error": 0.0000, "samples": 2, "value": [0.0, 0.0, 2.0], "label": "Leaf - 15", "type": "leaf"}]}, {"error": 0.0000, "samples": 43, "value": [0.0, 0.0, 43.0], "label": "Leaf - 16", "type": "leaf"}]}]}]}

Any clue on this error I'm getting in export.py line 60?
AttributeError: 'sklearn.tree._tree.Tree' object has no attribute 'init_error'

I'm getting the same error. Thoughts?

Same - let me know if you get past this

I installed an older version of scikit-learn (0.13.1) and ran it using Python 2.7. Rebooted my computer. Seems to work now. In PyCharm, you can select which version of a module you want to use under Preferences.

I am attempting to use this with Python 3.4 and Jupyter Notebooks. When I attempt to parse my .dot file I receive the following error:
AttributeError Traceback (most recent call last)
in ()
1 import tempfile
----> 2 out_file = tree.export_json(clf, out_file=tempfile.TemporaryFile())
3 out_file.close()

AttributeError: 'module' object has no attribute 'export_json'

I am new to sklearn and was wondering if there is anything else that I need to be doing?

I worked up a simple approach to this same thing: http://planspace.org/20151129-see_sklearn_trees_with_d3/ Possibly helpful as well?

Hey,

I need to have 2 decision trees in the same page. How can I do it?

Is there any tutorial on how to run the program?