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Merge branch 'master' of github.com:ageron/handson-ml

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Aurélien Geron 2018-08-11 12:21:11 +01:00
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08_dimensionality_reduction.ipynb
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@ -2606,7 +2606,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Another approach is to look at the _silhouette score_, which is the mean _silhouette coefficient_ over all the instances. An instance's silhouette coefficient is equal to $(a - b)/\\max(a, b)$ where $a$ is the mean distance to the other instances in the same cluster (it is the _mean intra-cluster distance_), and $b$ is the _mean nearest-cluster distance_, that is the mean distance to the instances of the next closest cluster (defined as the one that minimizes $b$, excluding the instance's own cluster). The silhouette coefficient can vary between -1 and +1: a coefficient close to +1 means that the instance is well inside its own cluster and far from other clusters, while a coefficient close to 0 means that it is close to a cluster boundary, and finally a coefficient close to -1 means that the instance may have been assigned to the wrong cluster."
"Another approach is to look at the _silhouette score_, which is the mean _silhouette coefficient_ over all the instances. An instance's silhouette coefficient is equal to $(b - a)/\\max(a, b)$ where $a$ is the mean distance to the other instances in the same cluster (it is the _mean intra-cluster distance_), and $b$ is the _mean nearest-cluster distance_, that is the mean distance to the instances of the next closest cluster (defined as the one that minimizes $b$, excluding the instance's own cluster). The silhouette coefficient can vary between -1 and +1: a coefficient close to +1 means that the instance is well inside its own cluster and far from other clusters, while a coefficient close to 0 means that it is close to a cluster boundary, and finally a coefficient close to -1 means that the instance may have been assigned to the wrong cluster."
]
},
{
@ -2697,7 +2697,7 @@
" coeffs = silhouette_coefficients[y_pred == i]\n",
" coeffs.sort()\n",
"\n",
" color = matplotlib.cm.spectral(i / k)\n",
" color = matplotlib.cm.Spectral(i / k)\n",
" plt.fill_betweenx(np.arange(pos, pos + len(coeffs)), 0, coeffs,\n",
" facecolor=color, edgecolor=color, alpha=0.7)\n",
" ticks.append(pos + len(coeffs) // 2)\n",