Update baseline_deep_dive.ipynb
Removed typo of s
"source": [
"### What is a baseline model? \n",
"\n",
- "Producing a baseline model is crucial for evaluating your model's performance on any machine learning problem. A baseline model is a basic solution to a machine learning problem that serves as a point of reference for comparing other models to. The baselines model's performance gives us an indication of how much better our models can perform relative to a naive approach. \n",
+ "Producing a baseline model is crucial for evaluating your model's performance on any machine learning problem. A baseline model is a basic solution to a machine learning problem that serves as a point of reference for comparing other models to. The baseline model's performance gives us an indication of how much better our models can perform relative to a naive approach. \n",
"\n",
"Let's say we are building a sentence similarity model where our training set contains pairs of sentences and we want to predict how similiar these sentences are on a scale from 1-5. We could spend months producing a complex machine learning solution to this problem and ultimately get a mean squared error (MSE) of 0.4. But is this result good or bad? There is no way of knowing without comparing it with some baseline performance. For our baseline model, we could predict the mean sentence similarity of sentence pairs in our training set (called the _zero rule_) and get a MSE of 0.2. So our model beats the baseline! If our model's performance was worse than the baseline, this might give us pause to consider using different features, models, evaluation metrics etc. It is crucial that the choice of baseline model be tailored to a data science problem based on buisness goals and the specific modeling task."
]
