Merge remote-tracking branch 'jakevdp/master'

Author: DevNambi

notebooks/02.00-Introduction-to-NumPy.ipynb

@@ -30,7 +30,7 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "THis chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
+ "This chapter, along with chapter 3, outlines techniques for effectively loading, storing, and manipulating in-memory data in Python.\n",
  "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else.\n",
  "Despite this apparent heterogeneity, it will help us to think of all data fundamentally as arrays of numbers.\n",
  "\n",

notebooks/05.03-Hyperparameters-and-Model-Validation.ipynb

@@ -430,7 +430,7 @@
  "![](figures/05.03-bias-variance-2.png)\n",
  "[figure source in Appendix](06.00-Figure-Code.ipynb#Bias-Variance-Tradeoff-Metrics)\n",
  "\n",
- "The score here is the $R^2$ score, or [coefficient of determineation](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\n",
+ "The score here is the $R^2$ score, or [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination), which measures how well a model performs relative to a simple mean of the target values. $R^2=1$ indicates a perfect match, $R^2=0$ indicates the model does no better than simply taking the mean of the data, and negative values mean even worse models.\n",
  "From the scores associated with these two models, we can make an observation that holds more generally:\n",
  "\n",
  "- For high-bias models, the performance of the model on the validation set is similar to the performance on the training set.\n",

notebooks/05.05-Naive-Bayes.ipynb

@@ -51,7 +51,7 @@
  "If we are trying to decide between two labels—let's call them $L_1$ and $L_2$—then one way to make this decision is to compute the ratio of the posterior probabilities for each label:\n",
  "\n",
  "$$\n",
- "\\frac{P(L_1~|~{\\rm features})}{P(L_1~|~{\\rm features})} = \\frac{P({\\rm features}~|~L_1)}{P({\\rm features}~|~L_2)}\\frac{P(L_1)}{P(L_2)}\n",
+ "\\frac{P(L_1~|~{\\rm features})}{P(L_2~|~{\\rm features})} = \\frac{P({\\rm features}~|~L_1)}{P({\\rm features}~|~L_2)}\\frac{P(L_1)}{P(L_2)}\n",
  "$$\n",
  "\n",
  "All we need now is some model by which we can compute $P({\\rm features}~|~L_i)$ for each label.\n",