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@andrewgiessel
Created September 22, 2012 19:26
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astro_ml_tutorial.ipynb
{
"metadata": {
"name": "astro_ml_tutorial"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Astro Machine Learning Tutorial\n",
"\n",
"This is a loose following of the following tutorial: http://astroml.github.com/sklearn_tutorial/index.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import YouTubeVideo\n",
"YouTubeVideo('33L_EXLtJPE')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"http://www.youtube.com/embed/33L_EXLtJPE\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"output_type": "pyout",
"prompt_number": 165,
"text": [
"<IPython.lib.display.YouTubeVideo at 0xa830c0c>"
]
}
],
"prompt_number": 165
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pprint"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Linear model fitting/classification"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.datasets import load_iris\n",
"iris = load_iris()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print iris['DESCR']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Iris Plants Database\n",
"\n",
"Notes\n",
"-----\n",
"Data Set Characteristics:\n",
" :Number of Instances: 150 (50 in each of three classes)\n",
" :Number of Attributes: 4 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" - sepal length in cm\n",
" - sepal width in cm\n",
" - petal length in cm\n",
" - petal width in cm\n",
" - class:\n",
" - Iris-Setosa\n",
" - Iris-Versicolour\n",
" - Iris-Virginica\n",
" :Summary Statistics:\n",
" ============== ==== ==== ======= ===== ====================\n",
" Min Max Mean SD Class Correlation\n",
" ============== ==== ==== ======= ===== ====================\n",
" sepal length: 4.3 7.9 5.84 0.83 0.7826\n",
" sepal width: 2.0 4.4 3.05 0.43 -0.4194\n",
" petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n",
" petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n",
" ============== ==== ==== ======= ===== ====================\n",
" :Missing Attribute Values: None\n",
" :Class Distribution: 33.3% for each of 3 classes.\n",
" :Creator: R.A. Fisher\n",
" :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
" :Date: July, 1988\n",
"\n",
"This is a copy of UCI ML iris datasets.\n",
"http://archive.ics.uci.edu/ml/datasets/Iris\n",
"\n",
"The famous Iris database, first used by Sir R.A Fisher\n",
"\n",
"This is perhaps the best known database to be found in the\n",
"pattern recognition literature. Fisher's paper is a classic in the field and\n",
"is referenced frequently to this day. (See Duda & Hart, for example.) The\n",
"data set contains 3 classes of 50 instances each, where each class refers to a\n",
"type of iris plant. One class is linearly separable from the other 2; the\n",
"latter are NOT linearly separable from each other.\n",
"\n",
"References\n",
"----------\n",
" - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
" Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
" Mathematical Statistics\" (John Wiley, NY, 1950).\n",
" - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
" (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n",
" - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
" Structure and Classification Rule for Recognition in Partially Exposed\n",
" Environments\". IEEE Transactions on Pattern Analysis and Machine\n",
" Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
" - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n",
" on Information Theory, May 1972, 431-433.\n",
" - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n",
" conceptual clustering system finds 3 classes in the data.\n",
" - Many, many more ...\n",
"\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x, y = iris.data, iris.target"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.svm import LinearSVC"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = LinearSVC(loss='l2')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 35,
"text": [
"LinearSVC(C=1.0, dual=True, fit_intercept=True, intercept_scaling=1,\n",
" loss='l2', multi_class=False, penalty='l2', scale_C=False, tol=0.0001)"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf = clf.fit(x, y)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'coef', clf.coef_\n",
"print 'intercept', clf.intercept_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"coef [[ 0.18423972 0.45122685 -0.8079411 -0.4507135 ]\n",
" [ 0.05196662 -0.89438541 0.40482774 -0.93782878]\n",
" [-0.85069859 -0.98674403 1.38090698 1.8652381 ]]\n",
"intercept [ 0.109561 1.67152933 -1.70965209]\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_new = [[5.0, 3.6, 1.3, 0.25]]\n",
"clf.predict(x_new)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 45,
"text": [
"array([0])"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import linear_model\n",
"from sklearn import tree"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d_tree = tree.DecisionTreeClassifier()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d_tree = d_tree.fit(x, y)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d_tree.predict(x_new)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 51,
"text": [
"array([0])"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clf2 = linear_model.LogisticRegression()\n",
"clf2.fit(x,y)\n",
"clf2.predict_proba(x_new)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 56,
"text": [
"array([[ 9.07512928e-01, 9.24770379e-02, 1.00343962e-05]])"
]
}
],
"prompt_number": 56
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"PCA and Clustering"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.decomposition import PCA\n",
"pca = PCA(n_components=2, whiten=True).fit(x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pca.components_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 61,
"text": [
"array([[ 0.17650757, -0.04015901, 0.41812992, 0.17516725],\n",
" [-1.33840478, -1.48757227, 0.35831476, 0.15229463]])"
]
}
],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pca.explained_variance_ratio_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 62,
"text": [
"array([ 0.92461621, 0.05301557])"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pca.explained_variance_ratio_.sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 63,
"text": [
"0.97763177502480314"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pca?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_pca = pca.transform(x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 66,
"text": [
"(150, 4)"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"X_pca.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 67,
"text": [
"(150, 2)"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.round(np.mean(X_pca))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 69,
"text": [
"0.0"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(3,3))\n",
"imshow(np.round(np.corrcoef(X_pca.T)))\n",
"io.embed()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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jpVksJpG0vLSYjpw6UprFYiIicVhMRCQOi4mIxClYizudm5tDT08PamtrEQ6HPdcMDg7i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\n",
"text": [
"<matplotlib.figure.Figure at 0xa617acc>"
]
}
],
"prompt_number": 164
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pylab as pl\n",
"from itertools import cycle\n",
"\n",
"def plot_pca_2d(data, target, target_names):\n",
" colors = cycle('rgbcmyk')\n",
" target_ids = range(len(target_names))\n",
" \n",
" for i, c, label in zip(target_ids, colors, target_names):\n",
" pl.scatter(data[target ==i, 0], data[target == i , 1], c=c, label=label)\n",
" pl.legend()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 87,
"text": [
"(150, 4)"
]
}
],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(5,3))\n",
"plot_pca_2d(X_pca, iris.target, iris.target_names)\n",
"\n",
"figure(figsize=(5,3))\n",
"x_bad = x[:,:2]\n",
"plot_pca_2d(x_bad, iris.target, iris.target_names)\n",
"\n",
"io.embed()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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xHj8e0tIqWp4FhWmtqlQXrZn5mZxJPWNRFpcVZ147u02uNpeL6RdZGruU6Lho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\n",
"text": [
"<matplotlib.figure.Figure at 0xa68332c>"
]
},
{
"output_type": "display_data",
"png": 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69es7be/QoUOMGDGCRo0acfLkSSRJYtq0aWzcuJHly5fb3tWRkZE0a9aMH3/8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\n",
"text": [
"<matplotlib.figure.Figure at 0xa75f5ec>"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.random.RandomState?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.cluster import KMeans\n",
"from numpy.random import RandomState\n",
"rng = RandomState(42)\n",
"\n",
"kmeans = KMeans(k=3, random_state=rng).fit(X_pca)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kmeans"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 105,
"text": [
"KMeans(copy_x=True, init='k-means++', k=3, max_iter=300, n_init=10,\n",
" precompute_distances=True,\n",
" random_state=<mtrand.RandomState object at 0x9a40400>, tol=0.0001,\n",
" verbose=0)"
]
}
],
"prompt_number": 105
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.round(kmeans.cluster_centers_, decimals=2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 106,
"text": [
"array([[ 1.02, -0.71],\n",
" [ 0.33, 0.89],\n",
" [-1.29, -0.44]])"
]
}
],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kmeans.labels_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 107,
"text": [
"array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
" 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
" 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1])"
]
}
],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(4,3))\n",
"plot_pca_2d(X_pca, kmeans.labels_, ['c0','c1','c2'])\n",
"figure(figsize=(4,3))\n",
"plot_pca_2d(X_pca, iris.target, iris.target_names)\n",
"io.embed()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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H+tCihS/du6uJj/eockTVmDFjWL16NatXr6Z///7s3bvXdr0CAqyLjrRaLbt2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\n",
"text": [
"<matplotlib.figure.Figure at 0xa82bb0c>"
]
},
{
"output_type": "display_data",
"png": 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2QRAY3mQ4G85vAODLTl+y9uxaDsUdAsDVyZVfB/xKC58W5mMKjYXmStwaV02J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\n",
"text": [
"<matplotlib.figure.Figure at 0xa82b3ec>"
]
}
],
"prompt_number": 108
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Fitting"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.random.seed(42)\n",
"x=np.random.random(20)\n",
"y=np.sin(2*x)\n",
"p = np.polyfit(x, y, 1)\n",
"print p"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 0.97896174 0.20367395]\n"
]
}
],
"prompt_number": 130
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_new = np.random.random(3)\n",
"y_new = np.polyval(p, x_new)\n",
"figure(figsize=(4,3))\n",
"plot(np.abs(x_new-y_new))\n",
"io.embed()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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yW3hpTyFGk4W08XG4aZznErI1Z85O8qYs5/kNFW0sX77cpu02HCnlaEkNz42N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\nEEI4MCnyQgjhwKTICyGEA/v/cGQc/CKq5swAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xaa1a6cc>"
]
}
],
"prompt_number": 131
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure(figsize=(3,3))\n",
"scatter(x,y)\n",
"scatter(x_new, y_new, color='red')\n",
"plot(np.linspace(0,1,100), np.polyval(p, np.linspace(0,1,100)))\n",
"io.embed()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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L6NGjB/v372fEiBHExsby3HPPYTQaefnllzGZTOzevZt33nmHqKgo7HY7w4YN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\n",
"text": [
"<matplotlib.figure.Figure at 0xa484e8c>"
]
}
],
"prompt_number": 132
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def test_func(x, err=0.5):\n",
" return np.random.normal(10 - 1. / (x + 0.1), err)\n",
"\n",
"def compute_error(x, y, p):\n",
" yfit=np.polyval(p, x)\n",
" return np.sqrt(np.mean((y-yfit) ** 2))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 154
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N= 8\n",
"np.random.seed(42)\n",
"x = 10 ** np.linspace(-2, 0, N)\n",
"y = test_func(x)\n",
"\n",
"x_fit = np.linspace(-0.2, 1.2, 1000)\n",
"\n",
"degrees = [1,2,6]\n",
"figure(figsize=(6,3))\n",
"for i, d in enumerate(degrees):\n",
" pl.subplot(131 + i, xticks=[], yticks=[])\n",
" pl.scatter(x, y, marker='x', c='k', s=50)\n",
" \n",
" p = np.polyfit(x, y, d)\n",
" y_fit = np.polyval(p, x_fit)\n",
" pl.plot(x_fit, y_fit, '-b')\n",
" \n",
" pl.xlim(-0.2, 1.2)\n",
" pl.ylim(0,12)\n",
" pl.xlabel('house size')\n",
" if i == 0:\n",
" pl.ylabel('price')\n",
" pl.title('N= '+ str(d))\n",
"io.embed() "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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hA03Jktxt2qRF48Y21K0rg1d+BXjjjUz4+/PYskUrdVPcVhyK8ZRcdc9LtCmK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\nTEyNXkRExMTU6EVEREzsbwLZXOEFyWg3AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa3b69ec>"
]
}
],
"prompt_number": 157
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"cross validation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Ntrain = 100\n",
"Ncrossval = 100\n",
"Ntest = 50\n",
"error = 1.0\n",
"\n",
"#randomly sample the data\n",
"np.random.seed(0)\n",
"x = np.random.random(Ntrain + Ncrossval + Ntest)\n",
"y = test_func(x, error)\n",
"\n",
"#select training set\n",
"xtrain = x[:Ntrain]\n",
"ytrain = y[:Ntrain]\n",
"\n",
"#select crossvalidation set\n",
"xcrossval = x[Ntrain:Ntrain + Ncrossval]\n",
"ycrossval = y[Ntrain:Ntrain + Ncrossval]\n",
"\n",
"#select testing set\n",
"xtest = x[Ntrain:-Ntest]\n",
"ytest = y[Ntrain:-Ntest]\n",
"\n",
"figure(figsize=(4,3))\n",
"pl.scatter(xtrain, ytrain, color='red')\n",
"pl.scatter(xcrossval, ycrossval, color='blue')\n",
"io.embed()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": 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Iz5fPSwIFSfEjHj2KdL7H7nQKIassL1CV44zSv80zt6zjm2/szJnjwOVSkzQ5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\n/idpGMzvAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xa66e12c>"
]
}
],
"prompt_number": 162
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"insert cross val code / learning curve code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"bias vs. variance in defining model parameters and determining data sampling\n",
"\n",
"note that sklearn has several cross-validation methods built-in\n",
"\n",
"\n",
"resume @ 1:57:00"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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